diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..5b6a065 --- /dev/null +++ b/.gitignore @@ -0,0 +1,4 @@ +.Rproj.user +.Rhistory +.RData +.Ruserdata diff --git a/Grieb-et-al-2023.Rproj b/Grieb-et-al-2023.Rproj new file mode 100644 index 0000000..d96a2b3 --- /dev/null +++ b/Grieb-et-al-2023.Rproj @@ -0,0 +1,15 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +StripTrailingWhitespace: Yes diff --git a/code/01_cellranger.R b/code/01_cellranger.R new file mode 100644 index 0000000..1d45e6d --- /dev/null +++ b/code/01_cellranger.R @@ -0,0 +1,129 @@ +library(dplyr) +library(yaml) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# scRNA-Seq: CellRanger count +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +nodes = 1 +ntasks = 1 +ttime = "44:00:00" +mail = "FAIL" +mem = 190000 +cpu = 25 + +manifest = yaml.load_file("manifest.yaml") + +# FASTQ files: NCBI GEO GSE234261 +fastqs = manifest$grieb$fastq + +# The FASTA sequences can be found in the supplementary material of the publication +ref.gex.cilta = paste0(manifest$base$workdata, "references/GRCh38-CiltaCel/") +ref.gex.ide = paste0(manifest$base$workdata, "references/GRCh38-IdeCel/") + +# VDJ reference: https://support.10xgenomics.com/single-cell-vdj/software/downloads/latest +ref.vdj = paste0(manifest$base$workdata, "references/refdata-cellranger-vdj-GRCh38-alts-ensembl-7.1.0/") + +# ADT reference can be found in the supplementary material of the publication or in "data/" of the repo +ref.features = "data/feature_reference.csv" +out.dir = paste0(manifest$base$workdata, "cohorts/grieb/cellranger/") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# sbatch +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +write_subscript = function(path, job_id, csv.out){ + file.create(path) + + write("#!/bin/bash", file = path, append = TRUE) + write("", file = path, append = TRUE) + + write(paste("#SBATCH -J", job_id), file = path, append = TRUE) + write(paste("#SBATCH --nodes", nodes), file = path, append = TRUE) + write(paste("#SBATCH --ntasks", ntasks), file = path, append = TRUE) + write(paste("#SBATCH --time", ttime), file = path, append = TRUE) + write(paste("#SBATCH --cpus-per-task", cpu), file = path, append = TRUE) + write(paste("#SBATCH --mem", mem), file = path, append = TRUE) + write(paste("#SBATCH --exclude=ribnode[009,020,006,007]"), file = path, append = TRUE) + write(paste("#SBATCH -e", paste0(job_id, ".e")), file = path, append = TRUE) + write(paste("#SBATCH -o", paste0(job_id, ".o")), file = path, append = TRUE) + write("#SBATCH --mail-type=END,FAIL", file = path, append = TRUE) + + write("", file = path, append = TRUE) + + write( + paste0( + "cellranger multi", + " --id ", job_id, + " --csv ", csv.out, + " --localcores=", cpu, + " --localmem=", mem/1000 + ), file = path, append = TRUE) + + write("", file = path, append = TRUE) +} + +write_multi_csv = function(sample.paths, csv.out){ + + rna = sample.paths[sample.paths$SOURCE == "Gene Expression", , drop = F] + adt = sample.paths[sample.paths$SOURCE == "Antibody Capture", , drop = F] + tcr = sample.paths[sample.paths$SOURCE == "VDJ-T", , drop = F] + bcr = sample.paths[sample.paths$SOURCE == "VDJ-B", , drop = F] + + file.create(csv.out) + + write(paste0("[gene-expression]"), file = csv.out, append = TRUE) + write(paste0("reference,", rna$GEX_REF), file = csv.out, append = TRUE) + write(paste0("[vdj]"), file = csv.out, append = TRUE) + write(paste0("reference,", ref.vdj), file = csv.out, append = TRUE) + write(paste0("[feature]"), file = csv.out, append = TRUE) + write(paste0("reference,", ref.features), file = csv.out, append = TRUE) + write(paste0("[libraries]"), file = csv.out, append = TRUE) + write(paste0("fastq_id,fastqs,feature_types"), file = csv.out, append = TRUE) + write(paste0(rna$SAMPLE, ",", rna$PATH, ",", rna$SOURCE), file = csv.out, append = TRUE) + write(paste0(adt$SAMPLE, ",", adt$PATH, ",", adt$SOURCE), file = csv.out, append = TRUE) + write(paste0(tcr$SAMPLE, ",", tcr$PATH, ",", tcr$SOURCE), file = csv.out, append = TRUE) + write(paste0(bcr$SAMPLE, ",", bcr$PATH, ",", bcr$SOURCE), file = csv.out, append = TRUE) + +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# sample paths +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +fastq.files = list.files(path = fastqs, full.names = T, recursive = T) +df = data.frame( + SAMPLE = basename(fastq.files), + PATH = dirname(fastq.files) +) +df$SAMPLE = gsub("_S.+", "", df$SAMPLE) +df = df %>% dplyr::mutate( + SOURCE = dplyr::case_when( + grepl("_R$", SAMPLE) ~ "Gene Expression", + grepl("_A$", SAMPLE) ~ "Antibody Capture", + grepl("_T$", SAMPLE) ~ "VDJ-T", + grepl("_B$", SAMPLE) ~ "VDJ-B" + ) +) +df$SAMPLE_SHORT = gsub("_R|_A|_T|_B", "", df$SAMPLE) + +# cilta-cel samples +cilta.samples = c("MXMERZ002A_03", "MXMERZ002A_19", "MXMERZ002A_20", "MXMERZ002A_04", "MXMERZ002A_08") +df$GEX_REF = gsub("_R|_A|_T|_B", "", df$SAMPLE_SHORT) %in% cilta.samples +df$GEX_REF = ifelse(df$GEX_REF == T, ref.gex.cilta, ref.gex.ide) + +df = df[!duplicated(df$SAMPLE), ] + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# submit to Slurm +# There is a link with this script under this path: "out.dir". +# The script was executed there. +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +for (sample in unique(df$SAMPLE_SHORT)) { + job_id = paste0("multi_", sample) + print(job_id) + csv.out = paste0(out.dir, job_id, ".csv") + sample.paths = subset(df, SAMPLE_SHORT == sample) + write_multi_csv(sample.paths, csv.out) + write_subscript(paste0(out.dir, job_id, ".slurm"), job_id, csv.out) + + cmd = paste0("sbatch ", paste0(out.dir, job_id, ".slurm")) + system(cmd) +} diff --git a/code/02_cellranger_to_seurat.R b/code/02_cellranger_to_seurat.R new file mode 100644 index 0000000..772b839 --- /dev/null +++ b/code/02_cellranger_to_seurat.R @@ -0,0 +1,99 @@ +print("# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") +print("Grieb") +print("# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Libraries +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +.cran_packages = c("Seurat", "yaml", "dplyr", "doParallel", "parallel", "data.table") +.bioc_packages = c("biomaRt", "scDblFinder", "SingleCellExperiment") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load Rawcounts and create a merged Seurat object +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") +work.path = manifest$grieb$data_dl +seurat.path = manifest$grieb$seurat +dir.create(seurat.path, recursive = T) + +obj.path = paste0(paste0(seurat.path, "seurat_ori_pub.Rds")) +obj.sub.path = paste0(paste0(seurat.path, "seurat_sub_ori_pub.Rds")) + +cellranger.dirs = list.dirs(path = paste0(work.path, "cellranger"), full.names = T, recursive = T) + +fltrd.dirs = cellranger.dirs[grepl("sample_filtered_feature_bc_matrix", cellranger.dirs)] +tmp2 = gsub(paste0(work.path, "cellranger/"), "", fltrd.dirs) +tmp2 = gsub("/", "", gsub("out.*", "", tmp2)) +tmp2 = gsub("multi_", "", tmp2) +names(fltrd.dirs) = tmp2 +fltrd.dirs = fltrd.dirs[!grepl("^bck", names(fltrd.dirs))] + +# i = names(fltrd.dirs)[1] +bpparam = BiocParallel::MulticoreParam(workers = 5) +seurat.l = BiocParallel::bplapply(names(fltrd.dirs), function(i) { + + id = i + fltrd.counts = Read10X(data.dir = fltrd.dirs[names(fltrd.dirs) == id], gene.column = 2) + + seu.obj = CreateSeuratObject(counts = fltrd.counts[[1]], project = id) + seu.obj[["ADT"]] = CreateAssayObject(counts = fltrd.counts[[2]]) + seu.obj@meta.data$orig.ident = id + seu.obj@meta.data$orig.ident = factor(seu.obj@meta.data$orig.ident) + + # scDblFinder + sce = scDblFinder(GetAssayData(seu.obj, slot="counts")) + stopifnot(identical(colnames(seu.obj), colnames(sce))) + seu.obj@meta.data$scDblFinder_score = sce$scDblFinder.score + seu.obj@meta.data$scDblFinder_class = sce$scDblFinder.class + + seu.obj + +}, BPPARAM = bpparam) + +seurat = merge(seurat.l[[1]], y = seurat.l[2:length(seurat.l)], add.cell.ids = names(seurat.l), project = "Grieb") +seurat@meta.data$orig.ident = factor(seurat@meta.data$orig.ident) + + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Assay ADT +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(seurat) = "ADT" +adt.ftrs = gsub("-proteona", "", rownames(seurat)) + +rownames(seurat@assays$ADT@counts) = toupper(adt.ftrs) +seurat@assays$ADT@data = seurat@assays$ADT@counts + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Save +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(seurat) = "RNA" +Idents(seurat) = "orig.ident" + +saveRDS(seurat, file = obj.path) +saveRDS(subset(x = seurat, downsample = 200), file = obj.sub.path) + diff --git a/code/03_qc_merge_seurat.R b/code/03_qc_merge_seurat.R new file mode 100644 index 0000000..e798a25 --- /dev/null +++ b/code/03_qc_merge_seurat.R @@ -0,0 +1,215 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Libraries and some Functions +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +.cran_packages = c( + "Seurat", "yaml", "dplyr", "stringr", "naturalsort", "data.table" +) +.bioc_packages = c() + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +source("code/helper/functions.R") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load objects and phenodata +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") +dir.create(manifest$meta$work, recursive = T) + +se.grie = readRDS(paste0(manifest$grieb$seurat, "seurat_ori_pub.Rds")) +output.file = paste0(manifest$meta$work, "seurat_pre_pub.Rds") + +pdata.grie = read.csv2(manifest$grieb$phenodata, sep = ",", na.strings="-", check.names = T) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Harmonize phenodata +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +consensus = c( + "STUDY", "PATIENT_ID", "AGE", "SEX", "PRODUCT", "SORTING", + "TIMEPOINT", "GROUP", "TISSUE_SOURCE", "RESPONSE_CONSENSUS", "RESPONSE" +) + +pdata.template = function(pd){ + pd = data.frame( + matrix(NA, nrow = nrow(pd), ncol=length(consensus)), + row.names = rownames(pd), + stringsAsFactors = F + ) + colnames(pd) = consensus + pd +} + +pdata = pdata.template(pdata.grie) %>% + dplyr::mutate( + STUDY = "Grieb et al.", + PATIENT_ID = pdata.grie$name, + AGE = pdata.grie$Age, + SEX = toupper(pdata.grie$sex), + SORTING = "None", + TISSUE_SOURCE = pdata.grie$source, + RESPONSE = pdata.grie$remission.after.CAR, + RESPONSE_CONSENSUS = dplyr::case_when( + pdata.grie$remission.after.CAR != "CR" ~ "nonCR", + TRUE ~ "CR" + ), + GROUP = dplyr::case_when( + pdata.grie$days.from.apharesis <= 1 ~ "Pre-infusion", + TRUE ~ "Post-infusion" + ), + TIMEPOINT = dplyr::case_when( + GROUP == "Post-infusion" ~ "Wk 3 to 4", + TRUE ~ "Pre" + ), + PRODUCT = dplyr::case_when( + PATIENT_ID %in% c("Patient 007", "Patient 008") ~ "cilta-cel", + TRUE ~ "ide-cel" + ), + TISSUE_SOURCE = dplyr::case_when( + TISSUE_SOURCE == "BM" ~ "BMMC", + TISSUE_SOURCE == "PB" ~ "PBMC", + TRUE ~ "something wrong" + ), + orig.ident = pdata.grie$SAMPLE_NAME + ) + +pdata = dplyr::left_join(se.grie@meta.data, pdata, by = "orig.ident") +rownames(pdata) = rownames(se.grie@meta.data) +se.grie@meta.data = pdata + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Harmonize CAR construct gene name +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +car.ftr = FetchData(se.grie, c("idecel", "ciltacel"), slot = "counts") +car.ftr = data.frame(car.ftr > 0) +citla.cells = rownames(se.grie@meta.data[se.grie$PRODUCT == "cilta-cel", ]) +ide.cells = rownames(se.grie@meta.data[se.grie$PRODUCT == "ide-cel", ]) + +car.exprs = se.grie@assays$RNA@counts[c("ciltacel", "idecel"), ] +car.exprs = car.exprs[1, , drop = F] + car.exprs[2, , drop = F] +rownames(car.exprs) = "CAR-BCMA" + +se.grie@assays$RNA@counts = rbind( + car.exprs, + se.grie@assays$RNA@counts[!rownames(se.grie) %in% c("ciltacel", "idecel"), ] +) +se.grie@assays$RNA@data = se.grie@assays$RNA@counts + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Add column in metadata wether CD4/CD8, CAR are present +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.grie = cd4cd8_car_present(se.grie) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Normalize +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.grie = NormalizeData(se.grie, assay = 'RNA', normalization.method = "LogNormalize") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Add %MT, %Ribosomal and complexity values +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.grie[["Perc_of_mito_genes"]] = Seurat::PercentageFeatureSet(se.grie, pattern = "^MT-") +se.grie[["Perc_of_ribosomal_genes"]] = Seurat::PercentageFeatureSet(se.grie, pattern = "^RPL|^RPS") +se.grie@meta.data$log10GenesPerUMI = log10(se.grie$nFeature_RNA) / log10(se.grie$nCount_RNA) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Some stats for plots +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pd.meta.pre = list( + se.grie@meta.data +) %>% + data.frame() + +saveRDS( + pd.meta.pre %>% dplyr::select( + orig.ident, nCount_RNA, nFeature_RNA, Perc_of_mito_genes, STUDY + ), + file = "data/metadata/stats_pre_filtering_pub.Rds" +) + +saveRDS( + pheno_finetuning( pd.meta.pre[!duplicated(pd.meta.pre$orig.ident), ] ), + file = "data/metadata/harmonized_phenodata_pre_filtering_pub.Rds" +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Cell filtering +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +nFeature_low_cutoff = 250 +nFeature_high_cutoff = 8000 +nCount_low_cutoff = 500 +nCount_high_cutoff = 100000 +mt_cutoff = 15 + +label_cells_rm = function(obj) { + + obj@meta.data = obj@meta.data %>% mutate( + KEEP_CELL_1 = case_when( + (nFeature_RNA < nFeature_low_cutoff) | (nFeature_RNA > nFeature_high_cutoff) | + (nCount_RNA < nCount_low_cutoff) | (nCount_RNA > nCount_high_cutoff) | + (Perc_of_mito_genes > mt_cutoff) ~ FALSE, + TRUE ~ TRUE + ) + ) + # print(table(obj$orig.ident, obj$KEEP_CELL_1)) + obj +} + +cell.track = count_cells_per_sample(c(se.grie)) +se.grie = label_cells_rm(se.grie) +se.grie = subset(se.grie, subset = KEEP_CELL_1 == TRUE) +se.grie@meta.data = droplevels(se.grie@meta.data) +se.grie$KEEP_CELL_1 = NULL +cell.track = count_cells_per_sample(c(se.grie), cell.track, "n1") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# CellCycleScoring +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +source("data/metadata/signatures/cellCycleMarkers.R") +se.grie = CellCycleScoring( + se.grie, s.features = s.genes, g2m.features = g2m.genes, + assay = 'RNA', search = TRUE +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Assay ADT +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(se.grie) = "ADT" +adt.ftrs = gsub("-proteona", "", rownames(se.grie)) + +rownames(se.grie@assays$ADT@counts) = adt.ftrs +se.grie@assays$ADT@data = se.grie@assays$ADT@counts + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("Save") +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(se.grie) = "RNA" +Idents(se.grie) = "orig.ident" +se.grie = pheno_finetuning(se.grie) + +se.grie +saveRDS(se.grie, output.file) + +saveRDS(cell.track, file = "data/metadata/harmonized_celltrack_pub.Rds") diff --git a/code/04_cell_annotation.R b/code/04_cell_annotation.R new file mode 100644 index 0000000..53c4b72 --- /dev/null +++ b/code/04_cell_annotation.R @@ -0,0 +1,298 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Libraries and some Functions +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +.cran_packages = c( + "Seurat", "yaml", "dplyr", "stringr", "naturalsort", "cowplot", "data.table", + "ggplot2", "ggthemes", "scGate", "patchwork", "Signac" +) +.bioc_packages = c("GEOquery", "UCell", "Rsamtools") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"ProjecTILs" %in% installed.packages())) { + remotes::install_github("carmonalab/ProjecTILs") +} +library(ProjecTILs) + +if (any(!"Azimuth" %in% installed.packages())) { + remotes::install_github('satijalab/azimuth', ref = 'master') +} +library(Azimuth) + +if (any(!"Azimuth" %in% installed.packages())) { + devtools::install_github('satijalab/seurat-data') +} +library(SeuratData) + +if (any(!"SeuratDisk" %in% installed.packages())) { + remotes::install_github("mojaveazure/seurat-disk") +} +library(SeuratDisk) + +source("code/helper/projectTils.R") +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") + +source("code/helper/azimuth_utilities.R") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load objects and phenodata +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +if (Sys.info()["nodename"] == "ribnode020") { + ncores = 25 +} else { + ncores = 5 +} + +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta, "seurat_pre_pub.Rds")) + +# se.meta = subset(x = se.meta, downsample = 200) +# se.meta = se.meta[, se.meta$PATIENT_ID == "Patient 001"] +# se.meta@meta.data = droplevels(se.meta@meta.data) + +output.file = paste0(manifest$meta$work, "seurat_pre_anno_pub.Rds") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("Filter step to capture only T-Cells (scGate)") +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +scGate_models_DB = get_scGateDB("data/metadata/scGateDB") +# library(ggparty) +# scGate::plot_tree(scGate_models_DB$human$generic$Tcell, box.size = 14, edge.text.size = 6) +tcell.model = scGate_models_DB$human$generic$Tcell + +sc_gating = function(obj) { + + obj.l = Split_Object(obj, split.by = "orig.ident", threads = ncores) + obj.l = parallel::mclapply(obj.l, function(x) { + x = scGate(x, model = tcell.model, assay = "RNA", slot = "data") + x@meta.data = x@meta.data[, !grepl("UCell|scGate_multi", colnames(x@meta.data))] + data.frame(barcode = rownames(x@meta.data), is.pure = x$is.pure) + }, mc.cores = ncores) + df = do.call("rbind", obj.l) + rownames(df) = df$barcode + df$barcode = NULL + obj = AddMetaData(obj, df) + obj +} + +se.meta = sc_gating(se.meta) +paste0(names(table(se.meta$is.pure)), ":", table(se.meta$is.pure), collapse = ", ") + +se.meta.t <- subset(se.meta, subset = `is.pure` == "Pure" ) +se.meta.t = subset(se.meta.t, subset = CD3_BY_EXPRS == "CD3") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("ProjecTILs") +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +if(!file.exists(paste0(manifest$base$workdata, "CD8T_human_ref_v1.rds"))){ + options(timeout = max(900, getOption("timeout"))) + download.file( + "https://figshare.com/ndownloader/files/38921366", + destfile = paste0(manifest$base$workdata, "CD8T_human_ref_v1.rds") + ) +} +if(!file.exists(paste0(manifest$base$workdata, "CD4T_human_ref_v1.rds"))){ + options(timeout = max(900, getOption("timeout"))) + download.file( + "https://figshare.com/ndownloader/files/39012395", + destfile = paste0(manifest$base$workdata, "CD4T_human_ref_v1.rds") + ) +} +ref.cd8 <- load.reference.map(paste0(manifest$base$workdata, "CD8T_human_ref_v1.rds")) +ref.cd4 <- load.reference.map(paste0(manifest$base$workdata, "CD4T_human_ref_v1.rds")) + +data(cell.cycle.obj) +scGate_model.cd4 <- ref.cd4@misc$scGate[["human"]] +scGate_model.cd8 <- ref.cd8@misc$scGate[["human"]] + +se.meta.t = ProjecTILs_worflow(se.obj = se.meta.t, threads = ncores) +se.meta.t$ProjecTILs_2 = ifelse( + se.meta.t$CD4CD8_BY_EXPRS == "CD4+CD8+", + "Not Estimable", + as.character(se.meta.t$ProjecTILs) +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("gamma delta T-cells") +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +my_scGate_model <- gating_model(name = "T_GD", signature = c("TRDC", "TRGC1", "TRGC2", "TRDV1")) +sc_gating = function(obj) { + obj.l = Split_Object(obj, split.by = "orig.ident", threads = 10) + obj.l = parallel::mclapply(obj.l, function(x) { + x = scGate(x, model = my_scGate_model, assay = "RNA", slot = "data") + x@meta.data = x@meta.data[, !grepl("UCell|scGate_multi", colnames(x@meta.data))] + data.frame(barcode = rownames(x@meta.data), is.pure = x$is.pure) + }, mc.cores = ncores) + df = do.call("rbind", obj.l) + rownames(df) = df$barcode + df$barcode = NULL + obj = AddMetaData(obj, df) + obj +} +se.meta.t = sc_gating(se.meta.t) + +se.meta.t@meta.data$ProjecTILs_2 = case_when( + se.meta.t@meta.data$is.pure == "Pure" & se.meta.t@meta.data$ProjecTILs_2 == "Not Estimable" ~ "gdT", + TRUE ~ se.meta.t@meta.data$ProjecTILs_2 +) +se.meta.t@meta.data = se.meta.t@meta.data %>% mutate( + ProjecTILs_2 = case_when( + ProjecTILs_2 == "Not Estimable" & CD4CD8_BY_EXPRS == "CD4+CD8-" ~ "CD4", + ProjecTILs_2 == "Not Estimable" & CD4CD8_BY_EXPRS == "CD4-CD8+" ~ "CD8", + TRUE ~ ProjecTILs_2 + ) +) + +se.meta@meta.data$ProjecTILs = se.meta.t$ProjecTILs[match(rownames(se.meta[[]]), rownames(se.meta.t[[]]))] +se.meta@meta.data$ProjecTILs_2 = se.meta.t$ProjecTILs_2[match(rownames(se.meta[[]]), rownames(se.meta.t[[]]))] +se.meta@meta.data$ProjecTILs_CONF = se.meta.t$ProjecTILs_CONF[match(rownames(se.meta[[]]), rownames(se.meta.t[[]]))] +se.meta@meta.data$ProjecTILs_LIN = se.meta.t$ProjecTILs_LIN[match(rownames(se.meta[[]]), rownames(se.meta.t[[]]))] + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load References +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +if(!file.exists(paste0(manifest$base$workdata, "/pbmc_multimodal.h5seurat"))) { + download.file( + url = "https://atlas.fredhutch.org/data/nygc/multimodal/pbmc_multimodal.h5seurat", + destfile = paste0(manifest$base$workdata, "/pbmc_multimodal.h5seurat") + ) + pb.ref = LoadH5Seurat(paste0(manifest$base$workdata, "/pbmc_multimodal.h5seurat")) +} else { + pb.ref = LoadH5Seurat(paste0(manifest$base$workdata, "/pbmc_multimodal.h5seurat")) +} + +if(!any(InstalledData()$Dataset %in% "bmcite")){ + InstallData("bmcite") + bm.ref = bmcite +} else { + bm.ref = LoadData(ds = "bmcite") +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("Anno: PBMC") +# https://satijalab.org/seurat/articles/multimodal_reference_mapping.html +# Example 1: Mapping human peripheral blood cells +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +anno_pb = function(query){ + query@meta.data = droplevels(query@meta.data) + query = SCTransform(query, verbose = T) + anchors = FindTransferAnchors( + reference = pb.ref, + query = query, + normalization.method = "SCT", + reference.reduction = "spca", + dims = 1:50 + ) + + query = TransferData( + anchorset = anchors, + reference = pb.ref, + query = query, + refdata = list(PB_CT_L2 = "celltype.l2") + ) + query +} + +DefaultAssay(se.meta) = "RNA" +se.meta.pbmc = subset(se.meta, subset = TISSUE_SOURCE == "PBMC") +se.pbmc.l = SplitObject(se.meta.pbmc, split.by = "orig.ident") + +se.pbmc.l= parallel::mclapply(se.pbmc.l, function(se){ + anno_pb(se) +}, mc.cores=length(se.pbmc.l)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("Anno: Bone Marrow") +# https://satijalab.org/seurat/articles/multimodal_reference_mapping.html +# Example 2: Mapping human bone marrow cells +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +anno_bm = function(query){ + # Query object is already normalized. Here, the reference was normalized using + # log-normalization (same as query object) + query@meta.data = droplevels(query@meta.data) + anchors = FindTransferAnchors( + reference = bm.ref, + query = query, + k.filter = NA, + reference.neighbors = "spca.annoy.neighbors", + reference.reduction = "spca", + dims = 1:50 + ) + + query = TransferData( + anchorset = anchors, + reference = bm.ref, + query = query, + refdata = list(BM_CT_L2 = "celltype.l2") + ) + query +} + +bm.ref = ScaleData(bm.ref, assay = 'RNA') +bm.ref = RunSPCA(bm.ref, assay = 'RNA', graph = 'wsnn') +bm.ref = FindNeighbors( + object = bm.ref, + reduction = "spca", + dims = 1:50, + graph.name = "spca.annoy.neighbors", + k.param = 50, + cache.index = TRUE, + return.neighbor = TRUE, + l2.norm = TRUE +) + +se.meta.bm = subset(se.meta, subset = TISSUE_SOURCE != "PBMC") +se.bm.l = SplitObject(se.meta.bm, split.by = "orig.ident") + +se.bm.l = parallel::mclapply(se.bm.l, function(se){ + anno_bm(se) +}, mc.cores=length(se.bm.l)) + +l = c(se.pbmc.l, se.bm.l) +se.meta = merge(l[[1]], l[2:length(l)]) +DefaultAssay(se.meta) = "RNA" +se.meta@assays[["SCT"]] = NULL +se.meta$nCount_SCT = NULL +se.meta$nFeature_SCT = NULL +se.meta@meta.data = se.meta@meta.data %>% + mutate(CT_L2 = ifelse(TISSUE_SOURCE == 'PBMC', predicted.PB_CT_L2, predicted.BM_CT_L2)) +se.meta@meta.data = se.meta@meta.data %>% + mutate(CT_L2_SCORE = ifelse(TISSUE_SOURCE == 'PBMC', predicted.PB_CT_L2.score, predicted.BM_CT_L2.score)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta = pheno_finetuning(se.meta) +se.meta@meta.data = droplevels(se.meta@meta.data) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +print("Save") +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta +saveRDS(se.meta, output.file) + + diff --git a/code/05_integration.R b/code/05_integration.R new file mode 100644 index 0000000..694329e --- /dev/null +++ b/code/05_integration.R @@ -0,0 +1,273 @@ +.cran_packages = c( + "yaml", "ggplot2", "reshape2", "dplyr", "foreach", "naturalsort", "ggthemes", + "cowplot", "clustree", "devtools", "scales", "stringr", "harmony", "MetBrewer", + "Seurat", "future", "scCustomize", "scGate" +) +.bioc_packages = c("dittoSeq") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"SeuratWrappers" %in% installed.packages())) { + remotes::install_github('satijalab/seurat-wrappers') +} +library(SeuratWrappers) + +if (any(!"SignatuR" %in% installed.packages())) { + remotes::install_github("carmonalab/SignatuR") +} +library(SignatuR) + +if (any(!"ProjecTILs" %in% installed.packages())) { + remotes::install_github("carmonalab/ProjecTILs") +} +library(ProjecTILs) + +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +theme_set(mytheme()) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + + +se.meta = readRDS(paste0(manifest$meta$work, "seurat_pre_anno_pub.Rds")) +dir.create(paste0(manifest$meta$work, "integration/"), recursive = T) +output.file = paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds") +output.file.t = paste0(manifest$meta$work, "integration/seurat_harmony_t_pub.Rds") + +se.meta = pheno_finetuning(se.meta) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Remove estimated doublets and red blood cells +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta = subset(se.meta, subset = scDblFinder_class == "singlet") +se.meta = se.meta[, !se.meta$CT_L2 %in% c("Eryth", "Platelet", "Prog_RBC", "Doublet")] + +# Harmonize between PBMC and BMMC +se.meta@meta.data = se.meta@meta.data %>% + mutate( + CT_L2 = case_when( + grepl("Naive B", CT_L2) ~ "B naive", + grepl("Memory B", CT_L2) ~ "B memory", + grepl("CD56 bright NK", CT_L2) ~ "NK_CD56bright", + TRUE ~ CT_L2 + ) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Replace Azimuth annotation with ProjecTILs annotation +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# cycling cell will be annotate separately +se.meta$celltype = trimws(gsub(" Proliferating", "", se.meta$CT_L2)) + +se.meta@meta.data = se.meta@meta.data %>% + mutate( + celltype = case_when( + grepl("^CD4|Treg", celltype) ~ "T-Cell", + grepl("^CD8", celltype) ~ "T-Cell", + grepl("dnT|gdT|MAIT|T Prolif", celltype) ~ "T-Cell", + TRUE ~ celltype + ) + ) + +se.meta@meta.data = se.meta@meta.data %>% mutate( + celltype = case_when( + is.pure == "Pure" ~ ProjecTILs_2, + TRUE ~ celltype + ) +) +se.meta@meta.data = se.meta@meta.data %>% mutate( + celltype_keep = case_when( + is.pure == "Pure" ~ "keep", + celltype == "T-Cell" ~ "rm", + is.pure != "Pure" & CT_L2_SCORE >= .5 ~ "keep", + TRUE ~ "rm" + ) +) + +se.meta = subset(se.meta, subset = celltype_keep == "keep"); se.meta$celltype_keep = NULL + +# Pure (scGate, T-Cells), but ProjectTILs annotation is contrary to CD4/CD8 expression +se.meta = se.meta[, !is.na(se.meta$celltype)] + +se.meta = subset(se.meta, subset = celltype != "Not Estimable") +se.meta@meta.data = droplevels(se.meta@meta.data) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Short annotation names for predcted cell identites +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta@meta.data = se.meta@meta.data %>% + mutate( + celltype_short_2 = case_when( + grepl("^CD4", celltype) ~ "CD4 T-Cell", + grepl("^CD8", celltype) ~ "CD8 T-Cell", + grepl("gdT", celltype) ~ "gdT-Cell", + grepl("dnT", celltype) ~ "dnT-Cell", + TRUE ~ celltype + ) + ) + +se.meta@meta.data = se.meta@meta.data %>% + mutate( + celltype_short_3 = case_when( + grepl("Eryth", celltype) ~ "Erythrocyte", + grepl("HSPC|CLP|EMP|GMP|LMPP|HSC|Prog_Mk|Prog_DC|^Prog_B", celltype) ~ "Progenitor", + grepl("ILC|BaEoMa|Stromal", celltype) ~ "Other", + grepl("B cell|Memory B|B memory|Naive B|B naive|pro B|pre B|transitional B|B intermediate", celltype) ~ "B-Cell", + grepl("Plasma|Plasmablast", celltype) ~ "Plasma cell", + grepl("^cDC", celltype) ~ "cDC", + grepl("^pDC", celltype) ~ "pDC", + grepl("^ASDC|^mDC|pre-pDC|pre-mDC", celltype) ~ "other DC", + grepl("^CD14", celltype) ~ "Mono CD14", + grepl("^CD16", celltype) ~ "Mono CD16", + grepl("Macrophage", celltype) ~ "Macrophage", + grepl("^NK|CD56 bright NK", celltype) ~ "NK", + grepl("^CD4", celltype) ~ "CD4 T-Cell", + grepl("^CD8", celltype) ~ "CD8 T-Cell", + grepl("gdT", celltype) ~ "gdT-Cell", + grepl("dnT", celltype) ~ "dnT-Cell", + TRUE ~ celltype + ) + ) + +cell.idents = sort(table(se.meta$celltype_short_3)) +se.meta = se.meta[, se.meta$celltype_short_3 %in% names(cell.idents[cell.idents > 100])] +se.meta@meta.data = droplevels(se.meta@meta.data) + +se.meta@meta.data$celltype = factor(se.meta@meta.data$celltype) +se.meta@meta.data$celltype_short_2 = factor(se.meta@meta.data$celltype_short_2) +se.meta@meta.data$celltype_short_3 = factor(se.meta@meta.data$celltype_short_3) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Integration +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dims.use.rna = 25 + +# RNA Integration +se.meta = integration( + obj = se.meta, + no.ftrs = 2000, + threads = 20, + .nbr.dims = dims.use.rna, + harmony.group.vars = c("orig.ident") +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Annotate CellCycle cluster +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +cl.res = "RNA_snn_res.1" + +data(cell.cycle.obj) +cc.phase = clustifyr::run_gsea( + se.meta@assays$RNA@data, query_genes = cell.cycle.obj$human$cycling, + cluster_ids = se.meta@meta.data[[cl.res]], n_perm = 1000 +) +cc.phase$pval_adj = p.adjust(cc.phase$pval, method = "BH") +cc.cl = rownames(cc.phase[cc.phase$pval < 0.1, ]) + +cc.cells = (se.meta@meta.data[[cl.res]] %in% cc.cl) +se.meta@meta.data$CellCycle = cc.cells + +(DimPlot_scCustom(se.meta, reduction = "umap", group.by = cl.res, pt.size = .1) & mytheme() & theme(legend.position = "none") | +DimPlot_scCustom(se.meta, reduction = "umap", group.by = "CellCycle", pt.size = .1) & mytheme() ) / + FeaturePlot_scCustom( + se.meta, + reduction = "umap", + features = c("S.Score", "G2M.Score"), + pt.size = .1, na_cutoff = .1, + colors_use = rev(MetBrewer::met.brewer("Hokusai1",n=100)) + ) & mytheme() + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# WNN Integration (ADT + RNA) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta = integraton_wnn(se.meta, dims.use.rna = dims.use.rna) + +DimPlot_scCustom( + se.meta, reduction = "umap", group.by = "celltype_short_3", raster = F, + pt.size = 0.01 +) | + DimPlot_scCustom( + se.meta, reduction = "adt.umap", group.by = "celltype_short_3", raster = F, + pt.size = 0.01 + ) | + DimPlot_scCustom( + se.meta, reduction = "wnn.umap", group.by = "celltype_short_3", raster = F, + pt.size = 0.01 + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Save +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(se.meta) = "RNA" +saveRDS(se.meta, output.file) +# se.meta = readRDS(output.file) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# T-cells +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dims.rna = 20 + +se.meta.t = se.meta[, grepl("^CD4|^CD8|^gdT", se.meta$celltype_short_3)] +se.meta.t@meta.data = droplevels(se.meta.t@meta.data) + +se.meta.t = integration( + obj = se.meta.t, + no.ftrs = 1000, + threads = 20, + .nbr.dims = dims.rna, + harmony.group.vars = c("orig.ident") +) + +# DimPlot_scCustom(se.meta.t, reduction = "umap", group.by = "celltype", pt.size = .1, colors_use = til.col) + +cl.res = "RNA_snn_res.0.8" + +data(cell.cycle.obj) +cc.phase = clustifyr::run_gsea( + se.meta.t@assays$RNA@data, query_genes = cell.cycle.obj$human$cycling, + cluster_ids = se.meta.t@meta.data[[cl.res]], n_perm = 1000 +) +cc.phase$pval_adj = p.adjust(cc.phase$pval, method = "BH") +cc.cl = rownames(cc.phase[cc.phase$pval < 0.1, ]) + +cc.cells = (se.meta.t@meta.data[[cl.res]] %in% cc.cl) +se.meta.t@meta.data$CellCycle = cc.cells + +(DimPlot_scCustom(se.meta.t, reduction = "umap", group.by = cl.res, pt.size = .1) & mytheme() & theme(legend.position = "none") | + DimPlot_scCustom(se.meta.t, reduction = "umap", group.by = "CellCycle", pt.size = .1) & mytheme() ) / + FeaturePlot_scCustom( + se.meta.t, + reduction = "umap", + features = c("S.Score", "G2M.Score"), + pt.size = .1, na_cutoff = .1, + colors_use = rev(MetBrewer::met.brewer("Hokusai1",n=100)) + ) & mytheme() + +saveRDS(se.meta.t, output.file.t) diff --git a/code/06_clonotyping_t.R b/code/06_clonotyping_t.R new file mode 100644 index 0000000..73a81a5 --- /dev/null +++ b/code/06_clonotyping_t.R @@ -0,0 +1,108 @@ +.cran_packages = c( + "yaml", "ggplot2", "reshape2", "dplyr", "foreach", "scico", + "naturalsort", "ggthemes", "cowplot", "clustree", "devtools", "scales", + "stringr", "clustree", "MetBrewer", "Seurat", "future", "scCustomize" +) +.bioc_packages = c("dittoSeq") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"scRepertoire" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("ncborcherding/scRepertoire") +} +library(scRepertoire) + +source("code/helper/styles.R") +source("code/helper/functions_plots.R") +source("code/helper/functions.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load Rawcounts and create a merged Seurat object +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_t_pub.Rds")) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.meta = subset(se.meta, subset = STUDY == "Grieb et al.") +se.meta@meta.data = droplevels(se.meta@meta.data) + +fltrd.vdj = list.files(path = manifest$grieb$data_dl, pattern = "filtered_contig_annotations.csv", full.names = T, recursive = T) +fltrd.vdj = fltrd.vdj[grepl("vdj_t/", fltrd.vdj)] +tmp = gsub(paste0(manifest$grieb$data_dl, "/cellranger/"), "", fltrd.vdj) +tmp = gsub("/", "", gsub("out.*", "", tmp)) +names(fltrd.vdj) = gsub("multi_", "", tmp) + +fltrd.vdj = fltrd.vdj[names(fltrd.vdj) %in% se.meta$orig.ident] + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +contig_list <- lapply(fltrd.vdj, function(x) { + tryCatch(read.csv(x), error=function(e) NULL) +}) +print(paste0("Empty file: ", names(lengths(contig_list)[lengths(contig_list) == 0]))) + +pheno = se.meta@meta.data +pheno$TMP = gsub(".+_", "", rownames(pheno)) +for (i in names(contig_list)) { + p = subset(pheno, orig.ident == i) + # p = subset(p, is.pure == "Pure") + print(nrow(contig_list[[i]])) + contig_list[[i]] = contig_list[[i]][contig_list[[i]]$barcode %in% p$TMP, ] + print(nrow(contig_list[[i]])) +} + +combined <- combineTCR( + contig_list, + samples = paste0(names(contig_list)) +) + +# CTstrict +max.clonotypes = max(unlist(lapply(combined, function(x){max(unname(table(x$CTstrict)))}))) + +se.meta <- combineExpression( + combined, se.meta, + cloneCall = "strict", + group.by = "sample", + proportion = F, + cloneTypes=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=max.clonotypes) +) +lvls = c( + paste0("Hyperexpanded (100 < X <= ", max.clonotypes, ")"), + "Large (20 < X <= 100)", + "Medium (5 < X <= 20)", + "Small (1 < X <= 5)", + "Single (0 < X <= 1)" +) +se.meta@meta.data$cloneType = factor(se.meta@meta.data$cloneType, levels = lvls) + +saveRDS(se.meta, paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes.Rds")) +saveRDS(combined, paste0(manifest$meta$work, "integration/scRepertoire_combined.Rds")) diff --git a/code/07_clonotyping_b.R b/code/07_clonotyping_b.R new file mode 100644 index 0000000..eff8a89 --- /dev/null +++ b/code/07_clonotyping_b.R @@ -0,0 +1,107 @@ +.cran_packages = c( + "yaml", "ggplot2", "reshape2", "dplyr", "foreach", "scico", + "naturalsort", "ggthemes", "cowplot", "clustree", "devtools", "scales", + "stringr", "clustree", "MetBrewer", "Seurat", "future", "scCustomize" +) +.bioc_packages = c("dittoSeq") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"scRepertoire" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("ncborcherding/scRepertoire") +} +library(scRepertoire) + +source("code/helper/styles.R") +source("code/helper/functions_plots.R") +source("code/helper/functions.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Load Rawcounts and create a merged Seurat object +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +fltrd.vdj = list.files(path = manifest$grieb$data_dl, pattern = "filtered_contig_annotations.csv", full.names = T, recursive = T) +fltrd.vdj = fltrd.vdj[grepl("vdj_b/", fltrd.vdj)] +tmp = gsub(paste0(manifest$grieb$data_dl, "/cellranger/"), "", fltrd.vdj) +tmp = gsub("/", "", gsub("out.*", "", tmp)) +names(fltrd.vdj) = gsub("multi_", "", tmp) + +fltrd.vdj = fltrd.vdj[names(fltrd.vdj) %in% se.meta$orig.ident] + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +contig_list <- lapply(fltrd.vdj, function(x) { + tryCatch(read.csv(x), error=function(e) NULL) +}) +print(paste0("Empty file: ", names(lengths(contig_list)[lengths(contig_list) == 0]))) +contig_list = contig_list[!lengths(contig_list) == 0] + +pheno = se.meta@meta.data +pheno$TMP = gsub(".+_", "", rownames(pheno)) +for (i in names(contig_list)) { + p = subset(pheno, orig.ident == i) + print(nrow(contig_list[[i]])) + contig_list[[i]] = contig_list[[i]][contig_list[[i]]$barcode %in% p$TMP, ] + print(nrow(contig_list[[i]])) +} + +combined <- combineBCR( + contig_list, + samples = paste0(names(contig_list)) +) + +# CTstrict +max.clonotypes = max(unlist(lapply(combined, function(x){max(unname(table(x$CTstrict)))}))) + +se.meta <- combineExpression( + combined, se.meta, + cloneCall = "strict", + group.by = "sample", + proportion = F, + cloneTypes=c(Single=1, Small=5, Medium=20, Large=100, Hyperexpanded=max.clonotypes) +) +lvls = c( + paste0("Hyperexpanded (100 < X <= ", max.clonotypes, ")"), + "Large (20 < X <= 100)", + "Medium (5 < X <= 20)", + "Small (1 < X <= 5)", + "Single (0 < X <= 1)" +) +se.meta@meta.data$cloneType = factor(se.meta@meta.data$cloneType, levels = lvls) + +se.meta = se.meta[, !is.na(se.meta$barcode)] +se.meta@meta.data = droplevels(se.meta@meta.data) + +saveRDS(se.meta, paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes_b.Rds")) diff --git a/code/figure_scripts/figure_02.R b/code/figure_scripts/figure_02.R new file mode 100644 index 0000000..97ba845 --- /dev/null +++ b/code/figure_scripts/figure_02.R @@ -0,0 +1,338 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", + "rlang", "patchwork", "cowplot", "scattermore" +) +.bioc_packages = c() + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +source("code/helper/adt_rna_gene_mapping.R") +# theme_set(mytheme(base_size = 5)) +base.size = 8 + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) +se.meta$PATIENT_ID_SHORT = gsub("Patient 0", "P_", se.meta$PATIENT_ID) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ct.reduc.pl = + dimreduc_celltypes( + obj = se.meta, ncol3 = 2, ncol2 = 2, base.size = base.size, leg.size = 3, + raster = T, raster.pt.size = .5 + ) + + theme( + legend.position = "bottom", + legend.margin = margin(l = 0), + legend.key.size = unit(3.25, "mm"), + legend.text = element_text(margin = margin(r = 4, unit = "pt")) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Barplot: metadata nbr. of cells per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +p.data = se.meta@meta.data +p.data = p.data[!duplicated(p.data$orig.ident), ] + +p.data.sub = p.data %>% + dplyr::select(orig.ident, GROUP, RESPONSE_CONSENSUS, PRODUCT) %>% + dplyr::mutate_if(is.factor, as.character) +p.data.sub = reshape2::melt(p.data.sub, id = "orig.ident") +p.data.sub$value = gsub("-infusion", "", p.data.sub$value) + +p.data.sub$GROUP = se.meta$GROUP[match(p.data.sub$orig.ident, se.meta$orig.ident)] +p.data.sub$GROUP = gsub("-infusion", "", p.data.sub$GROUP) +p.data.sub$variable = as.character(p.data.sub$variable) +p.data.sub$variable[p.data.sub$variable == "GROUP"] = "Infusion" +p.data.sub$variable[p.data.sub$variable == "RESPONSE_CONSENSUS"] = "Outcome" +p.data.sub$variable[p.data.sub$variable == "PRODUCT"] = "Product" +p.data.sub$TISSUE_SOURCE = se.meta$TISSUE_SOURCE[match(p.data.sub$orig.ident, se.meta$orig.ident)] +p.data.sub$value = factor(p.data.sub$value, levels = c("Pre", "Post", "CR", "nonCR", "cilta-cel", "ide-cel")) +y.order = p.data %>% arrange(GROUP, RESPONSE_CONSENSUS, desc(PATIENT_ID_SHORT)) +p.data.sub$orig.ident = factor(p.data.sub$orig.ident, levels = as.character(y.order$orig.ident)) +y.labels = setNames(as.character(y.order$PATIENT_ID_SHORT), as.character(y.order$orig.ident)) + +# grouped lengend +legend.col = setNames( + colors_use.10[1:6], + c("Pre", "Post", "CR", "nonCR", "cilta-cel", "ide-cel") +) + +p.data.sub$variable +legend.l = list() +for (i in unique(p.data.sub$variable)) { + select_plot = + ggplot(subset(p.data.sub, variable == i), aes(x = variable, y = orig.ident, fill = value)) + + geom_col( position="fill") + + scale_fill_manual(name = i, values = legend.col) + + mytheme(base_size = base.size) + + theme( + legend.justification = "left", + legend.text = element_text(size = rel(1)), + legend.key.size = unit(.65, "lines") + ) + legend.l[[i]] <- cowplot::get_legend(select_plot) +} + +p.data.pl = + ggplot(p.data.sub, aes(variable, orig.ident , fill = value)) + + geom_tile(color = "white", size = 1) + + facet_grid(TISSUE_SOURCE ~ ., scales = "free", space = "free") + + scale_y_discrete(labels = y.labels) + + scale_x_discrete(expand = c(0, 0)) + + scale_fill_manual(values = legend.col) + + mytheme(base_size = base.size) + + theme( + axis.title.y = element_blank(), + axis.title.x = element_blank(), + legend.position = "none", + panel.border = element_blank(), + axis.ticks = element_blank(), + axis.text.x = element_blank(), + strip.text.y = element_blank(), + axis.text.y = element_blank() + # axis.text.y = element_text(size = rel(0.78)) + ) + +p.data = se.meta@meta.data +p.data.ct = p.data %>% group_by(orig.ident, celltype_short_3) %>% dplyr::summarise(nbr.cells = n()) +p.data.ct$TISSUE_SOURCE = se.meta$TISSUE_SOURCE[match(p.data.ct$orig.ident, se.meta$orig.ident)] +p.data.ct$orig.ident = factor(p.data.ct$orig.ident, levels = levels(p.data.sub$orig.ident)) +lvls = names(ct.col)[names(ct.col) %in% p.data.ct$celltype_short_3] +p.data.ct$celltype_short_3 = factor(p.data.ct$celltype_short_3, levels = lvls) + +nbr.celltypes.pl = + ggplot(p.data.ct, aes(nbr.cells, orig.ident, fill = celltype_short_3)) + + geom_bar(stat="identity", position="fill", width = .8) + + facet_grid(TISSUE_SOURCE ~ ., scales = "free", space = "free") + + scale_y_discrete(labels = y.labels) + + scale_x_continuous(expand = c(0, 0), breaks = breaks_pretty(n = 3)) + + scale_fill_manual(values = ct.col) + + xlab("Cell type fraction") + + mytheme(base_size = base.size) + + theme( + axis.title.y = element_blank(), + panel.border = element_blank(), + legend.position = "none", + # axis.text.y = element_blank(), + axis.text.y = element_text(size = rel(0.78)), + axis.ticks.y = element_blank() + ) + +nbr.cells = reshape2::melt(table(se.meta$orig.ident)) +p.data.sub$NBR_CELLS = nbr.cells$value[match(p.data.sub$orig.ident, nbr.cells$Var1)] +nbr.cells.pl = + ggplot(p.data.sub[!duplicated(p.data.sub$orig.ident), ], aes(NBR_CELLS/1000, orig.ident)) + + geom_bar(stat="identity", fill = "#4D4D4D", width = .8) + + facet_grid(TISSUE_SOURCE ~ ., scales = "free", space = "free") + + scale_y_discrete(labels = y.labels) + + scale_x_continuous(expand = c(0, 0), breaks = breaks_pretty(n = 4)) + + xlab("Nbr. of cells (K)") + + mytheme(base_size = base.size) + + theme( + axis.title.y = element_blank(), + axis.text.y = element_blank(), + # axis.text.x = element_text(size = rel(0.78)), + # axis.title.x = element_text(size = rel(0.78)), + axis.ticks.y = element_blank(), + strip.text.y = element_blank() + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc: ADT marker +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +DefaultAssay(se.meta) = "ADT" + +VariableFeatures(se.meta) <- rownames(se.meta[["ADT"]])[!rownames(se.meta[["ADT"]]) %in% c("FITC", "PE", "TCRV2")] +se.meta = NormalizeData(se.meta, normalization.method = 'CLR', margin = 2) +adt_to_plot=c( + 'CD3E', 'CD4', 'CD8A', 'CD11C', 'CD14', 'CD16', 'CD19', "CD28", 'CD33', 'CD38', + 'CD39', 'CD56', "CD94", 'CD117', "CD123", 'HLA-DR' +) + +new.label = c(adt.rna.mapping, setNames("CD3E" , "CD3E"))[adt_to_plot] +names(adt_to_plot) = new.label + +# global.max = quantile(unlist(FetchData(se.meta, adt_to_plot)), .99999) +global.max = max(FetchData(se.meta, adt_to_plot)) + +adt.ftrs.l = dimreduc_features( + .obj = se.meta, features = adt_to_plot, .assay = "ADT", base.size = base.size, + .title.size = 1, .colors = scico(30, palette = 'oslo', direction = -1), .reduc = "wnn.umap", + .quantile.fltr = F, order = F, .x.title = NULL, .y.title = NULL, legend.wh = c(.3, 3), + plot.grid = F, min.max = c(0, (global.max)), .raster.scattermore = T, .raster.scattermore.pointsize = .5 +) +legend = get_legend(adt.ftrs.l[[1]]) +adt.ftrs.l = lapply(adt.ftrs.l, function(x){ + x = x + theme(legend.position='none') +}) + +adt.ftrs = plot_grid(plotlist = adt.ftrs.l, ncol = 8, scale = .95) +adt.ftrs = plot_grid(adt.ftrs, ggdraw(legend), rel_widths = c(1, .025), nrow = 1) + +DefaultAssay(se.meta) = "RNA" + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +cust.th = theme( + aspect.ratio = NULL, + axis.text = element_blank(), + axis.title = element_blank(), + panel.spacing = unit(1, "lines"), + axis.ticks = element_blank(), + legend.text = element_text(size = rel(1)), + legend.key.size = unit(3, "mm"), + legend.spacing.y = unit(1, 'mm'), + legend.title = NULL, + legend.justification = NULL, + panel.border = element_blank() +) + +pre.post.pl = + dimreduc_pheno(se.meta, .target = "PATIENT_ID_SHORT", .reduc = "wnn.umap", .col.pal.dicrete = colors_stata) + + cust.th + + facet_wrap( ~ GROUP, nrow = 2) + + guides(colour = guide_legend( + title = NULL, ncol = 1, override.aes = list(shape = 16, size = 2.5) + )) + +ts.pl = + dimreduc_pheno( + .obj = se.meta, .target = "TISSUE_SOURCE", .reduc = "wnn.umap", + .raster.scattermore = T, .raster.scattermore.pointsize = .5, + .col.pal.dicrete = c("#6699CC", "#997700") + ) + + mytheme(base_size = base.size) + + cust.th + + theme( + legend.position = "bottom", + plot.margin = margin(l=1, r=1, unit='mm'), + legend.margin = margin(t=2.5, b=0), + ) + + guides(colour = guide_legend( + title = "Source", ncol = 2, + override.aes = list(shape = 16, size = 3) + )) + +se.meta$CAR_BY_EXPRS_2 = ifelse(se.meta$CAR_BY_EXPRS == T, "yes", "no") +pr.pl = + dimreduc_pheno( + se.meta, .target = "CAR_BY_EXPRS_2", .reduc = "wnn.umap", + .raster.scattermore = T, .raster.scattermore.pointsize = .5, + .col.pal.dicrete = c("#6699CC", "#997700") + ) + + mytheme(base_size = base.size) + + cust.th + + theme( + legend.position = "bottom", + plot.margin = margin(l=1, r=1, unit='mm'), + legend.margin = margin(t=2.5, b=0), + ) + + guides(colour = guide_legend( + title = "CAR+", title.hjust = 0, ncol = 2, + override.aes = list(shape = 16, size = 3) + )) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc Density (splitted by outcome) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dim = "wnnUMAP" +pd = get_metadata(se.meta) %>% data.frame() +pd$DIM1 = pd[[paste0(dim, "_1")]] +pd$DIM2 = pd[[paste0(dim, "_2")]] + +outcome.dens = + ggplot(pd, aes(x = DIM1, y = DIM2)) + + scattermore::geom_scattermore(pointsize = 1) + + # geom_point(alpha = .1, size = .1) + + stat_density_2d(aes(fill = after_stat(density)), alpha = .7, geom = "raster", contour = F, show.legend = F) + + mytheme(base_size = base.size) + + theme( + # axis.text = element_blank(), + axis.ticks = element_blank(), + axis.title = element_blank(), + panel.spacing = unit(1, "lines"), + plot.margin = margin(0,1,3,1, unit='mm') + ) + + scale_y_discrete(expand = c(0,0)) + + scale_x_discrete(expand = c(0,0)) + + scico::scale_fill_scico(palette = "romaO", direction = -1, begin = 0, end = .8) + + facet_wrap(~ RESPONSE_CONSENSUS, nrow = 1) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Final plot +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +fin.pl = plot_grid( + plot_grid( + plot_grid(NULL, ct.reduc.pl + theme(legend.margin = margin(t = 1, b = -2)), NULL, nrow = 3, rel_heights = c(.01, 1, .01)), + NULL, + plot_grid( + NULL, + plot_grid( + plot_grid(nbr.celltypes.pl, nbr.cells.pl, p.data.pl, align = "h", nrow = 1, rel_widths = c(1.5, 1, .65)), + plot_grid(NULL, plot_grid(plotlist = legend.l, ncol = 1),NULL, nrow = 3, rel_heights = c(1,2,1)), + ncol = 2, rel_widths = c(1, .25) + ), + nrow = 2, rel_heights = c(.06, 1) + ), + NULL, + plot_grid( + NULL, + plot_grid(outcome.dens, plot_grid(ts.pl, pr.pl), nrow = 2, rel_heights = c(1.1, 1)), + NULL, + ncol = 1, rel_heights = c(.03, 1, .01) + ), + nrow = 1, rel_widths = c(.8, .075, .75, .075, .85), + labels = c("b", "", "c", "", "d"), label_fontface = "bold", label_size = 11 + ), + NULL, + # NULL, + plot_grid(adt.ftrs, labels = c("e"), label_fontface = "bold", label_size = 11, vjust = .5), + nrow = 3, rel_heights = c(1.5, .1, 1.1) +) + +ggsave2( + filename="code/figures/main/figure_02.png", + fin.pl, + width = 180, height = 115, dpi = 400, bg = "white", units = "mm", scale = 1.6 +) + +ggsave2( + filename="code/figures/main/figure_02.pdf", + fin.pl, + width = 180, height = 115, dpi = 300, bg = "white", units = "mm", scale = 1.6 +) + diff --git a/code/figure_scripts/figure_03.R b/code/figure_scripts/figure_03.R new file mode 100644 index 0000000..0525dfe --- /dev/null +++ b/code/figure_scripts/figure_03.R @@ -0,0 +1,280 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", + "rlang", "patchwork", "cowplot", "ggrepel", "scico", "parallel", "openxlsx" +) +.bioc_packages = c("org.Hs.eg.db", "clusterProfiler", "BiocParallel") + +if (any(!"enrichplot" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("YuLab-SMU/enrichplot") +} +library(enrichplot) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"presto" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("immunogenomics/presto") +} +library(presto) + +# if (any(!"muscat" %in% installed.packages())) { +# # Sys.unsetenv("GITHUB_PAT") +# devtools::install_github("HelenaLC/muscat", ref = "master") +# } +# library(muscat) + +if (any(!"speckle" %in% installed.packages())) { + remotes::install_github("phipsonlab/speckle", build_vignettes = TRUE, dependencies = "Suggest") +} +# browseVignettes("speckle") +library(speckle) + +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +theme_set(mytheme(base_size = 5)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) +se.meta = subset(se.meta, CAR_BY_EXPRS == F) + +se.post.bm = subset(se.meta, subset = TISSUE_SOURCE == "BMMC") +se.post.pb = subset(se.meta, subset = TISSUE_SOURCE == "PBMC" & GROUP == "Post-infusion") +se.post.bm@meta.data = se.post.bm@meta.data %>% + mutate( + celltype_short_2 = case_when( + grepl("Naive B", celltype) ~ "B naive", + grepl("Memory B", celltype) ~ "B memory", + grepl("CD56 bright NK", celltype) ~ "NK_CD56bright", + TRUE ~ celltype_short_2 + ) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Celltype composition per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +comp.pb.pl = comp_celltypes(se.post.pb@meta.data, base.size = 5, title.size = 6, group.psz = .2) + + theme( + legend.spacing.y = unit(0, "cm"), + legend.key.size = unit(0.3, "cm") + ) + + ggtitle("Comparision of post-infusional PBMC composition\nbetween nonCR and CR") + +comp.bm.pl = comp_celltypes(se.post.bm@meta.data, base.size = 5, title.size = 6, group.psz = .2) + + theme( + legend.spacing.y = unit(0, "cm"), + legend.key.size = unit(0.3, "cm") + ) + + ggtitle("Comparision of post-infusional BMMC composition\nbetween nonCR and CR") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# PBMC +obj = se.post.pb + +# table(obj$celltype_short_2, obj$RESPONSE_CONSENSUS) +res.pb = run_wilx( + obj = obj, target = "celltype_short_2", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.pb.sign = subset(res.pb, significant == T) +ct.keep = names(table(res.pb.sign$cluster)[table(res.pb.sign$cluster) > 10]) +res.pb.sign = res.pb.sign[res.pb.sign$cluster %in% ct.keep, ] +table(res.pb.sign$cluster) + +dgea.pb.pl = dgea_plot( + dgea.res = res.pb, dgea.res.sign = res.pb.sign, box.padding = .1, + base.size = 5, text.repel.size = 1.5, text.de.nbr.size = 1.75, text.cl.size = 1.75 +) +dgea.pb.pl = dgea.pb.pl + + ggtitle("DEG in post-infusional PBMCs comparing nonCR with CR\n") + + theme(plot.title = element_text(size = 6, hjust = 0.5, face = "plain")) + +# BMMC +obj = se.post.bm +obj = subset(obj, CAR_BY_EXPRS == F) +# table(obj$celltype_short_2, obj$RESPONSE_CONSENSUS) +res.bm = run_wilx( + obj = obj, target = "celltype_short_2", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.bm.sign = subset(res.bm, significant == T) +ct.keep = names(table(res.bm.sign$cluster)[table(res.bm.sign$cluster) > 10]) +res.bm.sign = res.bm.sign[res.bm.sign$cluster %in% ct.keep, ] +table(res.bm.sign$cluster) + +dgea.bm.pl = dgea_plot( + dgea.res = res.bm, dgea.res.sign = res.bm.sign, box.padding = .1, + base.size = 5, text.repel.size = 1.5, text.de.nbr.size = 1.75, text.cl.size = 1.75 +) +dgea.bm.pl = dgea.bm.pl + + ggtitle("DEG in post-infusional BMMCs comparing nonCR with CR\n") + + theme(plot.title = element_text(size = 6, hjust = 0.5, face = "plain")) + +# write to xlxs +write_to_xlsx(res.pb.sign, sheet = "post PBMC; nonCR vs CR", filename = "code/tables/Supplementaltable3.xlsx") +wb = loadWorkbook("code/tables/Supplementaltable3.xlsx") +write_to_xlsx(df = res.bm.sign, filename = "code/tables/Supplementaltable3.xlsx", sheet = "post BMMC; nonCR vs CR", wb = wb) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ORA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +df.ora = rbind( + res.pb.sign %>% mutate(SOURCE = "PBMC"), + res.bm.sign %>% mutate(SOURCE = "BMMC") +) +eg = bitr(res.pb$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +df.ora$ENTREZID = eg$ENTREZID[match(df.ora$feature, eg$SYMBOL)] +df.ora = df.ora[!is.na(df.ora$ENTREZID), ] + +df.ora$ID = paste0(df.ora$cluster, ".", df.ora$SOURCE) +geneList = lapply(split(df.ora, df.ora$ID), function(x){ + setNames(x$ENTREZID, x$logFC) +}) + +universe = bitr(res.pb$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +universe = universe[!is.na(universe$ENTREZID), ]$ENTREZID + +ora.cc <- compareCluster( + ENTREZID~cluster+SOURCE, + data = df.ora, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) +) +ora.cc = simplify(ora.cc, cutoff = .6) + +ora.pl = + ora_dotplot_cc( + ora.res = ora.cc, + genelist = geneList, + subset_cluster = "CD8 T-Cell|CD4 T-Cell", + order.by.p = F, + nbr.tops = 9, + term.length = 40, + gg.title = "", + base.size = 5, + max.value = 2, + min.size = 5, + x.text.size = rel(1), + facet = T, + facet.order = c(3, 2) +) + + scale_size(range = c(1, 3.5)) + + theme( + legend.position = "bottom", + axis.text.y = element_text(size = 4.5, lineheight = .75), + legend.spacing.x = unit(-0.25, 'mm'), + legend.margin = margin(r = 10), + legend.title = element_text(margin = margin(r = 2)) + ) + + guides( + fill = guide_colorbar( + title = "Z-score", barwidth = unit(2.5, 'lines'), title.vjust = 1.1, + barheight = unit(.25, 'lines'), order = 1, ticks.linewidth = 1.2/.pt + ), + size = guide_legend(title = "-Log10(FDR)", order = 2) + ) + +ora.all.pl = + ora_dotplot_cc( + ora.res = ora.cc, + genelist = geneList, + order.by.p = F, + nbr.tops = 10, + term.length = 72, + gg.title = "", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = T, + facet.order = c(3, 2), + dot.range = c(2, 6) + ) + ggtitle(NULL) + +ggsave2( + filename="code/figures/supplement/post_PBMC_nonCR_vs_CR_ora.pdf", + plot = ora.all.pl, bg = "white", + width = 180, height = 220, units = "mm", scale = 1.6 +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Final plot +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ct.comp.legend = get_legend(comp.pb.pl) +ora.legend = get_legend(ora.pl) +set.seed(123456) + +fin.pl = plot_grid( + plot_grid( + plot_grid( + NULL, dgea.bm.pl, NULL, dgea.pb.pl, nrow = 4, rel_heights = c(0.025, 1, .1, 1), + labels = c("", "a", "", "b"), label_fontface = "bold", label_size = 7 + ), + NULL, + plot_grid( + ora.pl + theme(legend.position = "none"), + plot_grid(ora.legend, NULL, rel_widths = c(1, 0)), + nrow = 2, rel_heights = c(1, .05), + labels = c("c", ""), label_fontface = "bold", label_size = 7, label_y = .99 + ), + ncol = 3, rel_widths = c(2, .1, 1) + ), + NULL, + plot_grid( + comp.bm.pl, + NULL, + comp.pb.pl, + nrow = 1, rel_widths = c(1, .1, 1), + labels = c("d", "", "e"), label_fontface = "bold", label_size = 7 + ), + nrow = 3, rel_heights = c(.66, .05, .33) +) + +ggsave2( + filename="code/figures/main/figure_03.png", + plot = fin.pl, + width = 180, height = 165, dpi = 300, bg = "white", units = "mm", scale = 1 +) + +ggsave2( + filename="code/figures/main/figure_03.pdf", + plot = fin.pl, + width = 180, height = 165, dpi = 300, bg = "white", units = "mm", scale = 1 +) diff --git a/code/figure_scripts/figure_04.R b/code/figure_scripts/figure_04.R new file mode 100644 index 0000000..233e07a --- /dev/null +++ b/code/figure_scripts/figure_04.R @@ -0,0 +1,392 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "openxlsx", + "rlang", "patchwork", "cowplot", "ggrepel", "scico", "parallel", "circlize" +) +.bioc_packages = c("org.Hs.eg.db", "clusterProfiler", "BiocParallel") + +if (any(!"enrichplot" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("YuLab-SMU/enrichplot") +} +library(enrichplot) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"presto" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("immunogenomics/presto") +} +library(presto) + +if (any(!"speckle" %in% installed.packages())) { + remotes::install_github("phipsonlab/speckle", build_vignettes = TRUE, dependencies = "Suggest") +} +# browseVignettes("speckle") +library(speckle) + +if (any(!"muscat" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("HelenaLC/muscat", ref = "master") +} +library(muscat) + +if (any(!"iTALK" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("Coolgenome/iTALK", build_vignettes = TRUE) +} +library(iTALK) + +if (any(!"liana" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + remotes::install_github('saezlab/liana') +} +library(liana) + +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +source("code/helper/italk_utilities.R") +source("code/helper/adt_rna_gene_mapping.R") +theme_set(mytheme(base_size = 8)) +base.size = 5 + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) +se.work = subset(se.meta, subset = TISSUE_SOURCE == "PBMC" & GROUP == "Pre-infusion") +se.work@meta.data = droplevels(se.work@meta.data) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Celltype composition per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +comp.pb.pl = comp_celltypes(se.work@meta.data, base.size = base.size, title.size = 6, group.psz = .2) + + theme( + legend.position = "bottom", + legend.spacing.y = unit(0, "cm"), + legend.key.size = unit(0.3, "cm") + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +obj = se.work +obj = subset(obj, CAR_BY_EXPRS == F) + +res.pb = run_wilx( + obj = obj, target = "celltype_short_2", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.pb.sign = subset(res.pb, significant == T) +ct.keep = names(table(res.pb.sign$cluster)[table(res.pb.sign$cluster) > 10]) +res.pb.sign = res.pb.sign[res.pb.sign$cluster %in% ct.keep, ] +table(res.pb.sign$cluster) + +# write to xlxs +wb = loadWorkbook("code/tables/Supplementaltable3.xlsx") +write_to_xlsx(df = res.pb.sign, filename = "code/tables/Supplementaltable3.xlsx", sheet = "pre PBMC; nonCR vs CR", wb = wb) + +dgea.pb.pl = dgea_plot( + dgea.res = res.pb, dgea.res.sign = res.pb.sign, + base.size = base.size, text.repel.size = 1.5, text.de.nbr.size = 1.75, text.cl.size = 1.75 +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ORA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +eg = bitr(res.pb.sign$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +res.pb.sign$ENTREZID = eg$ENTREZID[match(res.pb.sign$feature, eg$SYMBOL)] +geneList = lapply(split(res.pb.sign, res.pb.sign$cluster), function(x){ + x = x[!is.na(x$ENTREZID), ] + setNames(x$ENTREZID, x$logFC) +}) +universe = bitr(res.pb$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +universe = universe[!is.na(universe$ENTREZID), ]$ENTREZID + +ora.cc = compareCluster( + geneCluster = geneList, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) +) +ora.cc = simplify(ora.cc) + +ora.pl = + ora_dotplot_cc( + ora.res = ora.cc, + genelist = geneList, + order.by.p = F, + nbr.tops = 6, + term.length = 40, + gg.title = "", + base.size = base.size, + max.value = 1.5, + min.size = 5, + x.text.size = rel(1) + ) + + ggtitle(NULL) + + scale_size(range = c(1, 3.5)) + + theme( + axis.text.y = element_text(size = 4.5, lineheight = .75), + legend.key.size = unit(0.4, "cm") + ) + + guides( + fill = guide_colorbar( + title = "Z-score", barwidth = unit(.3, 'lines'), title.vjust = 1.1, + barheight = unit(3, 'lines'), order = 1, ticks.linewidth = 1.2/.pt + ), + size = guide_legend(title = "-Log10(FDR)", order = 2) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ADT expression (Mono, NK) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# performDE = function(obj){ +# +# DefaultAssay(obj) = "ADT" +# obj = NormalizeData(obj, normalization.method = 'CLR', margin = 2) +# +# sce = muscat::prepSCE( +# as.SingleCellExperiment(obj, assay='ADT'), +# kid = "celltype_short_2", sid = "orig.ident", +# gid = "RESPONSE", drop = F +# ) +# sce = muscat::aggregateData( +# sce, assay = "logcounts", fun = "median", +# by = c("cluster_id", "sample_id") +# ) +# +# l = list() +# for (i in names(sce@assays@data)) { +# res = presto::wilcoxauc(sce@assays@data[[i]], y = sce$RESPONSE_CONSENSUS) +# res = res[res$group == unique(res$group)[1], ] +# res$cluster = i +# l[[i]] = res +# } +# do.call("rbind", l) +# } +# +# adt.wilx = performDE(se.work) + +adt.wilx = run_wilx( + obj = obj, target = "celltype_short_2", assay = "ADT", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +adt.rna.mapping[adt.rna.mapping == "PTPRC"] = names(adt.rna.mapping[adt.rna.mapping == "PTPRC"]) +adt.wilx$feature_2 = adt.rna.mapping[match(adt.wilx$feature, names(adt.rna.mapping))] +adt.wilx = adt.wilx[adt.wilx$significant == T, ] + +adt.wilx.dcast = adt.wilx %>% reshape2::dcast(feature ~ cluster, value.var = 'logFC') +rownames(adt.wilx.dcast) = adt.wilx.dcast$feature +adt.wilx.dcast$feature = NULL +ftrs.order = list() +for (i in 1:nrow(adt.wilx.dcast)) { + ftrs.lfc = unlist(adt.wilx.dcast[i, , drop = T]) + ftrs.lfc = ftrs.lfc[!is.na(ftrs.lfc)] + ftrs.order[[i]] = (length(ftrs.lfc[ftrs.lfc > 0]) - length(ftrs.lfc[ftrs.lfc < 0])) / sqrt(ncol(adt.wilx.dcast)) +} +adt.wilx.dcast$order = unlist(ftrs.order) +adt.wilx.dcast = adt.wilx.dcast[order(adt.wilx.dcast$order, decreasing = F), ] + +adt.wilx$feature_2 = factor(adt.wilx$feature, levels = rownames(adt.wilx.dcast)) + +# max.value = max(abs(adt.wilx$logFC)) +dgea.pb.adt.pl = + ggplot(adt.wilx, aes(x = cluster, y = feature_2, size = -log10(padj), fill = logFC)) + + geom_point(colour="black", pch=21, stroke = .3) + + scale_size(range = c(.7, 2.7)) + + scale_fill_gradientn( + colours = cont.col, + limits = c( + -quantile(abs(adt.wilx$logFC), .99), + quantile(abs(adt.wilx$logFC), .99) + ), breaks = pretty_breaks(n = 3), + ) + + mytheme_grid(base_size = base.size) + + theme( + panel.grid.minor = element_blank(), + panel.grid.major.x = element_blank(), + axis.text.x = element_text(angle=45, vjust=1, hjust=1), + axis.text.y = element_text(size = 4.5), + legend.position = "right", + legend.key.size = unit(0.35, "cm") + ) + + guides( + fill = guide_colorbar( + title = "logFC", barwidth = unit(.3, 'lines'), + barheight = unit(3, 'lines'), order = 1, ticks.linewidth = 1.5/.pt + ), + size = guide_legend(title = "-Log10(FDR)", order = 2) + ) + + xlab(NULL) + ylab("Surface proteins") + +# se.mono = subset(se.work, subset = celltype_short_2 == "CD16 Mono") +# DefaultAssay(se.mono) = "ADT" +# samples = as.character(unique(se.mono$orig.ident)) +# names(samples) = se.work$RESPONSE_CONSENSUS[match(samples, se.work$orig.ident)] +# se.mono$orig.ident = factor(se.mono$orig.ident, levels = samples[order(names(samples))]) +# +# exprs.adt = GetAssayData(se.mono, slot='data', assay='ADT') +# exprs.adt = t(as.matrix(exprs.adt)) +# exprs.adt = cbind(exprs.adt, se.mono@meta.data %>% dplyr::select(RESPONSE_CONSENSUS, orig.ident)) +# exprs.adt = exprs.adt %>% +# tidyr::pivot_longer(cols=!c(RESPONSE_CONSENSUS, orig.ident), names_to='ADT', values_to='expression') +# exprs.adt = exprs.adt[exprs.adt$ADT == "CD39", ] +# p = round(subset(adt.wilx, feature == "CD39" & cluster == "CD16 Mono")$pval, 3) +# +# vln.mono = +# ggplot(exprs.adt, aes(x = orig.ident, y = expression, fill = RESPONSE_CONSENSUS)) + +# geom_violin(linewidth = 0.2, width = .75, scale = "width",) + +# geom_boxplot(lwd = .3, fatten = 2, width = .2, outlier.size = .5) + +# scale_fill_manual(values = c("CR" = "#6699CC", "nonCR" = "#997700")) + +# theme( +# legend.position = "right", +# legend.title = element_blank(), +# axis.text.x = element_blank(), +# axis.ticks.x = element_blank(), +# axis.title.x = element_blank(), +# plot.title = element_text(hjust = 0.5, face = "plain", colour = "black", size = rel(1)) +# ) + +# ggtitle('CD16 Mono | CD39') + +# annotate( +# geom = 'text', +# label = paste0("p: ", p), +# x = -Inf, y = Inf, hjust = -0.1, vjust = 1.4, size = 2.5 +# ) +# +# ### +# +# se.nk = subset(se.work, subset = celltype_short_2 == "NK") +# DefaultAssay(se.nk) = "ADT" +# samples = as.character(unique(se.nk$orig.ident)) +# names(samples) = se.work$RESPONSE_CONSENSUS[match(samples, se.work$orig.ident)] +# se.nk$orig.ident = factor(se.nk$orig.ident, levels = samples[order(names(samples))]) +# +# exprs.adt = GetAssayData(se.nk, slot='data', assay='ADT') +# exprs.adt = t(as.matrix(exprs.adt)) +# exprs.adt = cbind(exprs.adt, se.nk@meta.data %>% dplyr::select(RESPONSE_CONSENSUS, orig.ident)) +# exprs.adt = exprs.adt %>% +# tidyr::pivot_longer(cols=!c(RESPONSE_CONSENSUS, orig.ident), names_to='ADT', values_to='expression') +# exprs.adt = exprs.adt[exprs.adt$ADT == "CD94", ] +# p = round(subset(adt.wilx, feature == "CD94" & cluster == "NK")$pval, 3) +# +# vln.nk = ggplot(exprs.adt, aes(x = orig.ident, y = expression, fill = RESPONSE_CONSENSUS)) + +# geom_violin(linewidth = 0.2, width = .75, scale = "width",) + +# geom_boxplot(lwd = .3, fatten = 2, width = .2, outlier.size = .5) + +# scale_fill_manual(values = c("CR" = "#6699CC", "nonCR" = "#997700")) + +# theme( +# legend.position = "right", +# legend.title = element_blank(), +# axis.text.x = element_blank(), +# axis.ticks.x = element_blank(), +# axis.title.x = element_blank(), +# plot.title = element_text(hjust = 0.5, face = "plain", colour = "black", size = rel(1)) +# ) + +# ggtitle("NK | CD94") + +# annotate( +# geom = 'text', +# label = paste0("p: ", p), +# x = -Inf, y = Inf, hjust = -0.1, vjust = 1.4, size = 2.5 +# ) +# +# legend = get_legend(vln.mono + theme(legend.position = "bottom")) +# vln.mono = vln.mono + theme(legend.position = "none") +# vln.nk = vln.nk + theme(legend.position = "none") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Ligand-Receptor analysis +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +res.pb.sign.cp = res.pb.sign +res.pb.sign.cp$cluster = gsub("_CD56bright", "", res.pb.sign.cp$cluster) +lr.pl = ligand_receptor_analysis(dgea.res.sign = res.pb.sign.cp, font.size = base.size) +lr.pl = + lr.pl + + guides(colour = guide_legend( + title = "LFC Direction", title.position = "top", ncol = 1, order = 1, override.aes = list(shape = 16, size = 2.5) + )) + + scale_size(range = c(.7, 2.7)) + + theme( + legend.position = "right", + axis.text.y = element_text(size = 4.5), + legend.key.size = unit(0.35, "cm") + # strip.text.x = element_text(angle=45) + ) + + labs(fill = "logFC Direction") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Final plot +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +set.seed(333) + +fin.pl = plot_grid( + plot_grid( + NULL, + plot_grid(NULL, comp.pb.pl, nrow = 2, rel_heights = c( 0.05, 1)), + NULL, + plot_grid(NULL, dgea.pb.pl, nrow = 2, rel_heights = c(0.05, 1)), + nrow = 1, rel_widths = c(.0, .35, .03, .66), + labels = c("", "a", "", "b"), label_fontface = "bold", label_size = 7 + ), + NULL, + plot_grid( + plot_grid(ora.pl + xlab(""), labels = c("c"), label_fontface = "bold", label_size = 7, vjust = .1), + NULL, + plot_grid( + plot_grid( + NULL, dgea.pb.adt.pl, NULL, nrow = 1, rel_widths = c(.1, 1, .02), + labels = c("d", "", ""), label_fontface = "bold", label_size = 7, vjust = .1 + ), + NULL, + lr.pl, + nrow = 3, rel_heights = c(1, 0.05, 1.1), + labels = c("", "", "e"), label_fontface = "bold", label_size = 7 + ), + ncol = 3, rel_widths = c(1.3, .05, 1.8) + ), + nrow = 3, rel_heights = c(1.5, .15, 3.5) +) + +ggsave2( + filename="code/figures/main/figure_04.png", + fin.pl, + width = 180, height = 180, dpi = 300, bg = "white", units = "mm", scale = 1 +) + +ggsave2( + filename="code/figures/main/figure_04.pdf", + fin.pl, + width = 180, height = 180, dpi = 300, bg = "white", units = "mm", scale = 1 +) + + diff --git a/code/figure_scripts/figure_06.R b/code/figure_scripts/figure_06.R new file mode 100644 index 0000000..2180b5c --- /dev/null +++ b/code/figure_scripts/figure_06.R @@ -0,0 +1,711 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "ggalluvial", + "rlang", "remotes", "patchwork", "cowplot", "ggh4x", "ggrepel", "scico", "scCustomize", + "ggpubr" +) +.bioc_packages = c("dittoSeq", "SummarizedExperiment", "slingshot") + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"speckle" %in% installed.packages())) { + remotes::install_github("phipsonlab/speckle", build_vignettes = TRUE, dependencies = "Suggest") +} +# browseVignettes("speckle") +library(speckle) + +if (any(!"scRepertoire" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("ncborcherding/scRepertoire") +} +library(scRepertoire) + + +source("code/helper/styles.R") +source("code/helper/dgea_helper.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.t = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes.Rds")) +scRep.comb = readRDS(paste0(manifest$meta$work, "integration/scRepertoire_combined.Rds")) + +se.t@meta.data = se.t@meta.data %>% mutate( + celltype = case_when( + grepl("^CD4", celltype) & CellCycle == T ~ "CD4.Cycling", + grepl("^CD8", celltype) & CellCycle == T ~ "CD8.Cycling", + celltype == "CD8" ~ "CD8.other", + celltype == "CD4" ~ "CD4.other", + TRUE ~ celltype + ) +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dim = "UMAP" +ct.to.pl = "celltype" +pt.size = .1 +leg.size = 3.5 +leg.ncol = 1 +raster.pt.size = .5 + +pd = get_metadata(se.t) +pd$DIM1 = pd[[paste0(dim, "_1")]] +pd$DIM2 = pd[[paste0(dim, "_2")]] + +pd.cc = subset(pd, CellCycle == T) +pd.cc[[ct.to.pl]] = "Cycling" +pd = droplevels(pd[!pd$cell %in% pd.cc$cell, ]) + +pd.cd4 = pd[grepl("CD4", pd[[ct.to.pl]]), ] +pd.cd4[[ct.to.pl]] = factor( + pd.cd4[[ct.to.pl]], + levels = names(til.col[names(til.col) %in% pd.cd4[[ct.to.pl]]]) +) + +pd.cd8 = pd[grepl("CD8", pd[[ct.to.pl]]), ] +pd.cd8[[ct.to.pl]] = factor( + pd.cd8[[ct.to.pl]], + levels = names(til.col[names(til.col) %in% pd.cd8[[ct.to.pl]]]) +) + +pd.other = pd[!pd[[ct.to.pl]] %in% as.character(c(unique(pd.cd4[[ct.to.pl]]), unique(pd.cd8[[ct.to.pl]]))), ] +pd.other = rbind(pd.other, pd.cc) +pd.other[[ct.to.pl]] = factor( + pd.other[[ct.to.pl]], + levels = names(til.col[names(til.col) %in% pd.other[[ct.to.pl]]]) +) + +stopifnot( + length(colnames(se.t)) == ( nrow(pd.cd4) + nrow(pd.cd8) + nrow(pd.other)) +) + +ct.reduc.pl = + ggplot() + + scattermore::geom_scattermore(data = pd.cd4, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + + # geom_point(data = pd.cd4, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + + guides(colour = guide_legend( + title = "CD4", title.position = "top", ncol = 1, order = 2, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(til.col, setNames("#BBBBBB", "Cycling")), na.value = "green") + + ggnewscale::new_scale_color() + + scattermore::geom_scattermore(data = pd.cd8, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + + # geom_point(data = pd.cd8, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + + guides(colour = guide_legend( + title = "CD8", title.position = "top", ncol = 1, order = 1, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(til.col, setNames("#FFB92D", "Cycling")), na.value = "green") + + ggnewscale::new_scale_color() + + scattermore::geom_scattermore(data = pd.other, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + + # geom_point(data = pd.other, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + + guides(colour = guide_legend( + title = "Other", title.position = "top", ncol = 1, order = 3, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(til.col, setNames("#BBBBBB", "green")), na.value = "green") + + theme( + # aspect.ratio = 1, + legend.text = element_text(size = rel(0.78)), + legend.spacing.y = unit(1, 'mm'), + legend.key.size = unit(3, "mm"), + legend.position = "bottom", + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.border = element_blank(), + plot.title = element_text(size = rel(1.2), hjust = 0.5, face = "bold") + ) + + xlab("UMAP 1") + ylab("UMAP 2") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc and Compositin for CAR positive cells +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dim = "UMAP" +pd = get_metadata(se.t) +pd$DIM1 = pd[[paste0(dim, "_1")]] +pd$DIM2 = pd[[paste0(dim, "_2")]] + +pd = pd[order(pd$CAR_BY_EXPRS, decreasing = F, na.last = F), ] +pd$CAR_BY_EXPRS = ifelse(pd$CAR_BY_EXPRS == T, "yes", "no") + +reduc.car = + ggplot() + + scattermore::geom_scattermore(data = pd, aes(x = DIM1, y = DIM2, color = CAR_BY_EXPRS), pointsize = .75) + + scale_color_manual(values = c("#CC6677", "#DDCC77")) + + guides(colour = guide_legend( + title = "CAR+", title.position = "top", ncol = 1, + override.aes = list(shape = 16, size = leg.size) + )) + + theme( + legend.position = "right", + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.border = element_blank(), + axis.title = element_blank() + ) + + se.t.car = subset(se.t, subset = CAR_BY_EXPRS == T) + keep.samples = names(table(se.t.car$orig.ident)[table(se.t.car$orig.ident) > 10]) + se.t.car <- subset(se.t.car, subset = orig.ident %in% keep.samples) + se.t.car@meta.data = droplevels(se.t.car@meta.data) + df = dittoBarPlot( + se.t.car, "celltype", group.by = "orig.ident", color.panel = til.col, + main = NULL, legend.title = "Cell identities", split.by = "orig.ident", + theme = mytheme(base_size = 10), xlab = NULL, + retain.factor.levels = F, split.adjust = list(scales = "free_x"), data.out = T + ) + + dat = df$data + dat$PATIENT_ID = se.t.car$PATIENT_ID[match(dat$grouping, se.t.car$orig.ident)] + dat$PATIENT_ID = paste0(gsub("Patient 0", "P", dat$PATIENT_ID)) + x.labels = setNames(dat$PATIENT_ID, dat$grouping) + x.labels = x.labels[!duplicated(names(x.labels))] + + facet.labels.df = dat %>% dplyr::group_by(grouping) %>% dplyr::summarise(cells = sum(count)) + facet.labels.df$cells = paste0("n=", facet.labels.df$cells) + facet.labels.df$SOURCE = se.t.car$TISSUE_SOURCE[match(facet.labels.df$grouping, se.t.car$orig.ident)] + facet.labels.df$PRODUCT = se.t.car$PRODUCT[match(facet.labels.df$grouping, se.t.car$orig.ident)] + facet.labels.df$PRODUCT = gsub("-cel", "+", facet.labels.df$PRODUCT) + + facet.labels = paste0(facet.labels.df$SOURCE, "\n", facet.labels.df$PRODUCT, "\n", facet.labels.df$cells) + names(facet.labels) = facet.labels.df$grouping + + x.label = data.frame(table(se.t.car$orig.ident)) + se.t.car$FACET_TITLE = x.label$Freq[match(se.t.car$orig.ident, x.label$Var1)] + se.t.car$FACET_TITLE = paste0(se.t.car$TISSUE_SOURCE, "\nCAR+\n", se.t.car$FACET_TITLE) + se.t.car$AXIS_LABEL = paste0(gsub("Patient 0", "P", se.t.car$PATIENT_ID)) + x.label$TIMEPOINT = se.t.car$TIMEPOINT[match(x.label$Var1, se.t.car$orig.ident)] + + dat$label = ifelse(grepl("Cycling", dat$label), "Cycling", as.character(dat$label)) + +cell.comp.car = + ggplot(dat, aes(grouping, count, fill = label)) + + geom_col(aes(fill = label), position = "fill", width = .9) + + facet_grid( + ~ grouping, scales="free", space = "free", + labeller = labeller(grouping = facet.labels) + ) + + scale_fill_manual(values = til.col) + + scale_x_discrete(labels = x.labels) + + theme( + legend.position = "none", + axis.title.x = element_blank(), + strip.text = element_text(size = rel(.9), colour = "black"), + axis.text.x = element_text(angle=45, hjust=1, vjust = 1) + ) + + scale_y_continuous(breaks = c(0,0.5,1), expand = c(0, 0), limits = c(0, NA)) + + ylab("Percent of cells") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Clonotypes (Basic) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +clono.col <- length(levels(se.t$cloneType)) +clono.col = setNames(c(colorblind_vector(clono.col), "#BBBBBB"), c(levels(se.t$cloneType), NA)) + +dim = "UMAP" +pd = get_metadata(se.t) +pd$DIM1 = pd[[paste0(dim, "_1")]] +pd$DIM2 = pd[[paste0(dim, "_2")]] + +pd = pd[order(pd$cloneType, decreasing = T, na.last = F), ] + +reduc.cl = + ggplot() + + scattermore::geom_scattermore(data = pd, aes(x = DIM1, y = DIM2, color = cloneType), pointsize = 1) + + # geom_point(data = pd, aes(x = DIM1, y = DIM2, color = cloneType), shape = ".") + + guides(colour = guide_legend(title = "Clonotype\ngroups", nrow = 2, reverse= T, override.aes = list(shape = 16, size = leg.size))) + + scale_colour_manual(values = clono.col) + + theme( + legend.position = "bottom", + legend.margin = margin(l = 0), + legend.key.size = unit(3.5, "mm"), + legend.text = element_text(margin = margin(r = 7, unit = "pt")), + legend.title = element_text(margin = margin(r = 5, unit = "pt")), + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.border = element_blank() + ) + + xlab(NULL) + ylab(NULL) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Association between the number of T cell clonotypes and the number of cells +# per clonotype. +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +cl_abund = function( + se.obj, + source = "PBMC", + group = NULL, + split.by = "RESPONSE_CONSENSUS", + pl.title = NULL +) { + + se.obj = subset(se.t, TISSUE_SOURCE == source) + if(!is.null(group)){ + se.obj = subset(se.obj, GROUP == group) + } + se.obj@meta.data = droplevels(se.obj@meta.data) + + cl.abund = abundanceContig( + se.obj, + cloneCall = "strict", scale = F, + split.by = split.by, + exportTable = T + ) + + cl.abund = as.data.frame.matrix( + table(cl.abund$Abundance, cl.abund$values) + ) + + cl.abund$ABUNDANCE = as.numeric(rownames(cl.abund)) + cl.abund = reshape2::melt(cl.abund, id = "ABUNDANCE") + cl.abund$value[cl.abund$value == 0] = NA + + ggplot(cl.abund, aes((ABUNDANCE), (value), color = variable)) + + # geom_smooth(aes(group = variable, color = variable), se = F) + + scale_y_log10() + + scale_x_log10() + + geom_point(size = 1.25) + + # theme(aspect.ratio = 1) + + xlab("Nbr. of cells per clonotype") + + ylab("Nbr. of clonotypes") + + scale_fill_manual(values = c("#6699CC", "#997700")) + + scale_color_manual(values = c("#6699CC", "#997700")) + + theme( + legend.justification=c(1,.9), + legend.position=c(.95, .95), + legend.spacing.x = unit(.01, 'mm'), + legend.spacing.y = unit(.01, 'mm'), + legend.key.size = unit(3, "mm"), + legend.text = element_text(margin = margin(t = 0)), + legend.title=element_blank() + ) + + guides(colour = guide_legend( + override.aes = list(shape = 16, size = 2) + )) + + ggtitle(pl.title) +} + +cl.abund.pl.1 = cl_abund(se.obj = se.t, group = "Pre-infusion", pl.title = "PBMC | Pre-infusionial") +cl.abund.pl.2 = cl_abund(se.obj = se.t, group = "Post-infusion", pl.title = "PBMC | Post-infusionial") +cl.abund.pl.3 = cl_abund(se.obj = se.t, source = "BMMC", group = "Post-infusion", pl.title = "BMMC | Post-infusionial") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Clonotypes grouped by T-cell identities (CR and nonCR) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.pb = subset(se.t, TISSUE_SOURCE == "PBMC") +se.pb$TMP = paste0(se.pb$GROUP, "_", se.pb$RESPONSE_CONSENSUS) +occ.rep.pb = scRepertoire::occupiedscRepertoire( + se.pb, x.axis = "celltype", facet.by = c("TMP"), proportion = T, + exportTable = T +) %>% dplyr::mutate(SORUCE = "PBMC") + +se.bm = subset(se.t, TISSUE_SOURCE == "BMMC") +se.bm$TMP = paste0(se.bm$GROUP, "_", se.bm$RESPONSE_CONSENSUS) +occ.rep.bm = scRepertoire::occupiedscRepertoire( + se.bm, x.axis = "celltype", facet.by = c("TMP"), proportion = T, + exportTable = T +) %>% dplyr::mutate(SORUCE = "BMMC") + +occ.rep = rbind(occ.rep.pb, occ.rep.bm) + +occ.rep$GROUP = gsub("_.+", "", occ.rep$TMP) +occ.rep$GROUP = gsub("-infusion", "", occ.rep$GROUP) +occ.rep$GROUP = paste0(occ.rep$SORUCE, " | ", occ.rep$GROUP) +occ.rep$GROUP = factor(occ.rep$GROUP, levels = c("PBMC | Pre", "PBMC | Post", "BMMC | Post")) +occ.rep$RESPONSE_CONSENSUS = gsub(".+_", "", occ.rep$TMP) +occ.rep$RESPONSE_CONSENSUS = factor(occ.rep$RESPONSE_CONSENSUS, levels = c("nonCR", "CR")) + +df.sum = occ.rep %>% dplyr::group_by(celltype) %>% + dplyr::summarise(n = sum(value)) %>% + dplyr::filter(n < 100) %>% + data.frame() + +occ.rep = occ.rep[!occ.rep$celltype %in% c(df.sum$celltype, "CD4.other", "CD8.other"), ] +occ.rep$celltype = gsub("CD4.", "CD4\n", occ.rep$celltype) +occ.rep$celltype = gsub("CD8.", "CD8\n", occ.rep$celltype) +occ.rep$celltype = gsub("_", "\n", occ.rep$celltype) +occ.rep$celltype = gsub("NaiveLike", "Naive\nLike", occ.rep$celltype) +occ.rep.pl = + ggplot(occ.rep, aes(RESPONSE_CONSENSUS, value, fill = cloneType)) + + geom_col(position = "fill") + + facet_grid(GROUP ~ celltype) + + scale_fill_manual(values = clono.col) + + scale_y_continuous(breaks = c(0,0.5,1)) + + theme( + legend.position = "none", + axis.title.x = element_blank(), + axis.text.x = element_text(angle=45, hjust=1, vjust = 1), + axis.ticks.x = element_blank(), + strip.text.x = element_text(size = rel(1)), + ) + + ylab("Proportion of cells") + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Top clones +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +clonal.diversity = function( + se.sub = NULL, + lineage = NULL, + x.axis = "RESPONSE_CONSENSUS" +) { + se.sub = se.sub[, grepl(paste0("^", lineage), se.sub$celltype)] + se.sub@meta.data = droplevels(se.sub@meta.data) + + scRepertoire::clonalDiversity( + df = se.sub, + cloneCall = "strict", + split.by = "orig.ident", + group.by = "orig.ident", + x.axis = x.axis, + skip.boots = T, + exportTable = T + ) +} + +cl.div.pb.cd4.pre = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "PBMC" & GROUP == "Pre-infusion"), + lineage = "CD4" +) %>% dplyr::mutate(GROUP = "PBMC | Pre", LIN = "CD4") + +cl.div.pb.cd8.pre = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "PBMC" & GROUP == "Pre-infusion"), + lineage = "CD8" +) %>% dplyr::mutate(GROUP = "PBMC | Pre", LIN = "CD8") + +cl.div.pb.cd4.post = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "PBMC" & GROUP == "Post-infusion"), + lineage = "CD4" +) %>% dplyr::mutate(GROUP = "PBMC | Post", LIN = "CD4") + +cl.div.pb.cd8.post = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "PBMC" & GROUP == "Post-infusion"), + lineage = "CD8" +) %>% dplyr::mutate(GROUP = "PBMC | Post", LIN = "CD8") + +cl.div.bm.cd4.post = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "BMMC" & GROUP == "Post-infusion"), + lineage = "CD4" +) %>% dplyr::mutate(GROUP = "BMMC | Post", LIN = "CD4") + +cl.div.bm.cd8.post = clonal.diversity( + se.sub = subset(se.t, TISSUE_SOURCE == "BMMC" & GROUP == "Post-infusion"), + lineage = "CD8" +) %>% dplyr::mutate(GROUP = "BMMC | Post", LIN = "CD8") + +dat = do.call( + "rbind", + list( + cl.div.pb.cd4.pre, cl.div.pb.cd8.pre, cl.div.pb.cd4.post, cl.div.pb.cd8.post, + cl.div.bm.cd4.post, cl.div.bm.cd8.post + ) +) +dat$GROUP = factor(dat$GROUP, levels = c("PBMC | Pre", "PBMC | Post", "BMMC | Post")) +dat$PRODUCT = se.t$PRODUCT[match(dat$orig.ident, se.t@meta.data$orig.ident)] + +cl.div.pl = + ggplot(dat, aes(RESPONSE_CONSENSUS, Shannon, fill = RESPONSE_CONSENSUS)) + + geom_boxplot(outlier.shape = NA, fatten = 1.5, linewidth = .2) + + geom_point( + aes(group = RESPONSE_CONSENSUS), size=.5, + position = position_jitterdodge() + ) + + scale_fill_manual(values = c("CR" = "#6699CC", "nonCR" = "#997700")) + + ggpubr::stat_compare_means(label = "p.format", label.x = 1.1, label.y.npc = .95, size = 2.5) + + facet_grid(LIN ~ GROUP) + + xlab(NULL) + ylab("Shannon diversity") + + ylim(min(dat$Shannon), max(dat$Shannon) + .5) + + theme( + panel.spacing = unit(.75, "lines"), + legend.position = "none" + ) + +top_car_clones = function(patient = "Patient 012", pl.title = "Patient 012") { + + df = se.t@meta.data + df = df[!is.na(df$CTstrict), ] + df = subset(df, PATIENT_ID == patient & GROUP == "Post-infusion") + unname(head((sort(table(df$CTstrict), decreasing = T)), 10)) + top.clonotype = names(head(sort(table(df$CTstrict), decreasing = T), 10)) + df = df[df$CTstrict %in% top.clonotype, ] + + car = df[df$CAR_BY_EXPRS == T, ] + unname(sort(table(car$CTstrict), decreasing = T)) + + df = as.data.frame.matrix(table(df$CTstrict, df$CAR_BY_EXPRS)) + rownames(df) = NULL + colnames(df) = c("CAR-", "CAR+") + df = df[order(df$`CAR+`, decreasing = T), ] + rownames(df) = NULL + df$rank = rownames(df) + df.m = reshape2::melt(df, id = "rank") + df.m$rank = factor(df.m$rank, levels = naturalsort(unique(df.m$rank), decreasing = F)) + + ggplot(df.m, aes(x = rank, y = value, fill = variable)) + + geom_col(position = "fill") + + geom_text( + aes(label=value), position = position_fill(vjust= 0.5), + colour = "black", size = 2 + ) + + scale_y_continuous(breaks = c(0,0.5,1)) + + scale_fill_manual(values = c("#CC6677", "#DDCC77")) + + ylab("Proportion") + + xlab("Most abundant clonotypes") + + labs(fill = NULL) + + ggtitle(pl.title) +} + +top.clono.p12.1 = top_car_clones(patient = "Patient 012", pl.title = "Patient 012") +top.clono.p14.1 = top_car_clones(patient = "Patient 014", pl.title = "Patient 014") + + +se.t$TMP = ifelse(se.t$GROUP == "Pre-infusion", "1", "2") + +top.clono.p12.2 = + scRepertoire::compareClonotypes( + subset(se.t, PATIENT_ID == "Patient 012"), + split.by = "TMP", + numbers = 10, + exportTable = F +) + + mytheme(base_size = 8) + + theme(legend.position = "none") + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Proportion") + + xlab(NULL) + + scale_x_discrete(labels = setNames(c("Pre", "Post") , c("1", "2"))) + + ggtitle("Patient 012") + + geom_alluvium(color="black", linewidth = .01) + +top.clono.p14.2 = scRepertoire::compareClonotypes( + subset(se.t, PATIENT_ID == "Patient 014"), + split.by = "TMP", + numbers = 10, + exportTable = F +) + + mytheme(base_size = 8) + + theme(legend.position = "none") + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Proportion") + + xlab(NULL) + + scale_x_discrete(labels = setNames(c("Pre", "Post") , c("1", "2"))) + + ggtitle("Patient 014") + + geom_alluvium(color="black", linewidth = .01) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA: Cell identity marker +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.cd4 = se.t[, grepl("^CD4", se.t$celltype)] +se.cd4 = se.cd4[, !grepl("^CD4$", se.cd4$celltype)] +se.cd4 = se.cd4[, se.cd4$CAR_BY_EXPRS == F] +res.wlx.cd4 = run_wilx_ct_marker( + obj = se.cd4, + target = "celltype", + min.cells = 50, + downsample = F +) %>% dplyr::rename(celltype = group) + +mrkr.cd4 = mrkr_bubble( + dgea.res = res.wlx.cd4, + se.obj = se.cd4, + nbr.tops = 5, + pt.size = 3.5, + font.size = 7, + quantile.cut = .99, + order.by.effect.size = F, + aspectRatio = NULL +) + +se.cd8 = se.t[, grepl("^CD8", se.t$celltype)] +se.cd8 = se.cd8[, !grepl("^CD8$", se.cd8$celltype)] +se.cd8 = se.cd8[, se.cd8$CAR_BY_EXPRS == F] +res.wlx.cd8 = run_wilx_ct_marker( + obj = se.cd8, + target = "celltype", + min.cells = 50, + downsample = F +) %>% dplyr::rename(celltype = group) + +mrkr.cd8 = mrkr_bubble( + dgea.res = res.wlx.cd8, + se.obj = se.cd8, + nbr.tops = 5, + pt.size = 3.5, + font.size = 7, + quantile.cut = .99, + order.by.effect.size = F, + aspectRatio = NULL +) + +res.wlx = run_wilx_ct_marker( + obj = se.t[, se.t$CAR_BY_EXPRS == F], + target = "celltype", + min.cells = 50, + downsample = F +) %>% dplyr::rename(celltype = group) + +mrkr.all = mrkr_bubble( + dgea.res = res.wlx, + se.obj = se.t[, se.t$CAR_BY_EXPRS == F], + nbr.tops = 5, + pt.size = 3.5, + quantile.cut = .99, + font.size = 7, + order.by.effect.size = F, + aspectRatio = NULL, + export.table = T +) + +mrkr.gdT = mrkr_bubble( + dgea.res = res.wlx, + features = mrkr.all[mrkr.all$celltype == "gdT", ]$feature, + se.obj = se.t[, se.t$CAR_BY_EXPRS == F], + nbr.tops = 5, + pt.size = 3.5, + quantile.cut = .99, + font.size = 7, + order.by.effect.size = F, + aspectRatio = NULL +) + +supps = plot_grid( + plot_grid( + mrkr.cd4 + ggtitle("CD4 T-cells"), NULL, mrkr.cd8 + ggtitle("CD8 T-cells"), + ncol = 3, rel_widths = c(1, .05, 1.05), align = "vh" + ), + mrkr.gdT + ggtitle("Gamma delta T-cells"), + nrow = 2, rel_heights = c(1, .325) +) + +ggsave2( + filename="code/figures/supplement/tcell_ident_marker.pdf", + plot = supps, bg = "white", + width = 180, height = 225, units = "mm", scale = 1 +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +fig.c.d = + plot_grid( + plot_grid( + NULL, + plot_grid( + plot_grid( + NULL, reduc.cl + theme(legend.position = "none", plot.margin = margin(l = 0, r = 5, t = 5, b = 5)), + NULL, rel_heights = c(.1, 1, .15), nrow = 3, scale = 1 + ), + NULL, + plot_grid(cl.abund.pl.1, cl.abund.pl.2, cl.abund.pl.3, nrow = 1, scale = .95), + rel_widths = c(1, .0, 3.25), nrow = 1, + labels = c("c", "", "d"), label_fontface = "bold", label_size = 11, vjust = .8 + ), + nrow = 2, rel_heights = c(.05, 1) + ), + NULL, + plot_grid(NULL, get_legend(reduc.cl), NULL, nrow = 1, rel_widths = c(.25, 3, 1.5)), + nrow = 3, rel_heights =c(1, .05, 0.2) +) + +fig.b = plot_grid( + plot_grid( + NULL, + plot_grid( + NULL, + plot_grid(NULL, reduc.car + theme(legend.position = c(.8,.8)), rel_heights = c(.04, 1), nrow = 2), + ncol = 2, rel_widths = c(.075, 1) + ), + NULL, + nrow = 1, + rel_widths = c(.075, 1, .0) + # rel_widths = c(.45, 1, .15) + ), + cell.comp.car, + nrow = 2, rel_heights = c(1, 1.5) +) + +fig.g.h = plot_grid( + plot_grid(top.clono.p12.1 + theme(legend.position = "none"), NULL, top.clono.p12.2, nrow = 1, align = "h", rel_widths = c(1.4, .1, 1)), + plot_grid(top.clono.p14.1 + theme(legend.position = "none"), NULL, top.clono.p14.2, nrow = 1, align = "h", rel_widths = c(1.4, .1, 1)), + nrow = 2, labels = c("g", "h"), label_fontface = "bold", label_size = 11, vjust = .5 +) +fig.g.h = plot_grid( + fig.g.h, + plot_grid(get_legend(top.clono.p12.1 + theme(legend.position = "bottom", legend.key.size = unit(.3, 'cm'))), NULL, rel_widths = c(1.6, 1)), + nrow = 2, rel_heights = c(1, .02) +) + +fin.pl = + plot_grid( + plot_grid( + plot_grid( + NULL, + plot_grid(NULL, ct.reduc.pl, ncol = 1, rel_heights = c(.04, 1)), + NULL, ncol = 3, rel_widths = c(.075, 1, .0), + labels = c("a"), label_fontface = "bold", label_size = 11, label_y = .993, label_x = .05 + ), + NULL, + plot_grid(fig.b, NULL, nrow = 2, rel_heights = c(1, .025)), + ncol = 1, rel_heights = c(1.4, .05, 1.95), + labels = c("", "", "b"), label_fontface = "bold", label_size = 11 + ), + NULL, + plot_grid( + fig.c.d, + NULL, + occ.rep.pl, + NULL, + plot_grid( + plot_grid(plot_grid(NULL, cl.div.pl, rel_widths = c(.03, 1)), nrow = 2, rel_heights = c(1, .065)), + NULL, + fig.g.h, + NULL, + ncol = 4, rel_widths = c(1.2, .075 , 2, .025), + labels = c("f", "", "", ""), label_fontface = "bold", label_size = 11, vjust = 1, hjust = -1 + ), + NULL, + nrow = 6, rel_heights = c(.9, .03, 1.1, .1, 1, .01), + labels = c("", "", "e"), label_fontface = "bold", label_size = 11 + ), + ncol = 3, rel_widths = c(1.11, 0.15, 3), align = "vh" +) + +ggsave2( + filename="code/figures/main/figure_06.png", + fin.pl, + width = 180, height = 150, dpi = 300, bg = "white", units = "mm", scale = 1.6 +) + +ggsave2( + filename="code/figures/main/figure_06.pdf", + fin.pl, + width = 180, height = 150, dpi = 300, bg = "white", units = "mm", scale = 1.6 +) diff --git a/code/figure_scripts/figure_07.R b/code/figure_scripts/figure_07.R new file mode 100644 index 0000000..1d3c722 --- /dev/null +++ b/code/figure_scripts/figure_07.R @@ -0,0 +1,997 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "ggalluvial", + "rlang", "remotes", "patchwork", "cowplot", "ggh4x", "ggrepel", "scico", "ggrastr" +) +.bioc_packages = c( + "dittoSeq", "SummarizedExperiment", "scRepertoire", "org.Hs.eg.db", + "clusterProfiler", "ComplexHeatmap" +) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"enrichplot" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("YuLab-SMU/enrichplot") +} +library(enrichplot) + +if (any(!"speckle" %in% installed.packages())) { + remotes::install_github("phipsonlab/speckle", build_vignettes = TRUE, dependencies = "Suggest") +} +# browseVignettes("speckle") +library(speckle) + +source("code/helper/styles.R") +source("code/helper/dgea_helper.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +source("code/helper/adt_rna_gene_mapping.R") +adt.rna.mapping[adt.rna.mapping == "PTPRC"] = names(adt.rna.mapping[adt.rna.mapping == "PTPRC"]) +theme_set(mytheme(base_size = 8)) + +plot_marker = function( + obj = NULL, + obj.assay = "ADT", + wlx.sign = NULL, + ftr.labels = NULL, + grp = "GROUP" +){ + + obj@meta.data = droplevels(obj@meta.data) + + DefaultAssay(obj) = obj.assay + expr.data = FetchData( + obj, + wlx.sign$feature[!grepl("CD8A|CD4|TCRAB|CD14", wlx.sign$feature)], + slot = "data" + ) + stopifnot(identical(rownames(expr.data), rownames(obj@meta.data))) + expr.data = expr.data %>% + bind_cols(obj@meta.data) %>% + pivot_longer(cols =!c(colnames(obj@meta.data)) , names_to='FEATURE', values_to='EXPRS') %>% + data.frame() + + ftr.lvl = wlx.sign[order(wlx.sign$logFC, decreasing = T), ]$feature + expr.data$FEATURE = factor(expr.data$FEATURE, levels = ftr.lvl) + + pl = ggplot(expr.data, aes(x = orig.ident, y = EXPRS,fill = .data[[grp]]))+ + # geom_jitter(shape = ".", alpha = 0.5, width = .1, show.legend = F, color='#555555')+ + ggrastr::geom_jitter_rast(alpha = 0.5, width = .1, show.legend = F, color='#555555', shape = ".", raster.dpi = 100) + + geom_violin(linewidth = 0.2, alpha = .5) + + stat_summary(fun= "median",geom = "crossbar", width = 0.2, show.legend = F, lwd = .3)+ + scale_fill_manual(values = c("#994455", "#6699CC")) + + theme( + axis.text.x = element_blank(), + axis.ticks.x = element_blank(), + axis.title.x = element_blank(), + axis.text.y = element_text(size = rel(.6)), + axis.title.y = element_text(size = rel(.8)), + panel.spacing = unit(.75, "lines"), + plot.title = element_text(hjust = 0.5, face = "bold", colour = "black", size = rel(1)), + ) + + scale_y_continuous(breaks = pretty_breaks(3)) + + ylab("Expression") + + labs(fill = NULL) + if(!is.null(ftr.labels)) { + pl = pl + facet_wrap( ~ FEATURE, scales = "free", labeller = labeller(FEATURE = ftr.labels)) + } else { + pl = pl + facet_wrap( ~ FEATURE, scales = "free") + } + pl +} + +run_ora = function(df = NULL, df.sign = NULL){ + eg = bitr(df$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") + df.sign$ENTREZID = eg$ENTREZID[match(df.sign$feature, eg$SYMBOL)] + df.sign = df.sign[!is.na(df.sign$ENTREZID), ] + + geneList = lapply(split(df.sign, df.sign$cluster), function(x){ + setNames(x$ENTREZID, x$logFC) + }) + + universe = eg[!is.na(eg$ENTREZID), ]$ENTREZID + + ore.res <- compareCluster( + geneClusters = geneList, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) + ) + ore.res = simplify(ore.res) + + pl = ora_dotplot_cc( + ora.res = ore.res, + genelist = geneList, + order.by.p = F, + nbr.tops = 5, + gg.title = "", + base.size = 8, + min.size = 3, + x.text.size = rel(1), + facet = F + ) + plot(pl) + return(list(ore.res = ore.res, geneList = geneList)) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) +se.meta@meta.data = se.meta@meta.data %>% mutate( + celltype = case_when( + grepl("^CD4", celltype) & CellCycle == T ~ "CD4.Cycling", + grepl("^CD8", celltype) & CellCycle == T ~ "CD8.Cycling", + celltype == "CD8" ~ "CD8.other", + celltype == "CD4" ~ "CD4.other", + TRUE ~ celltype + ) +) +se.meta$celltype = factor(se.meta$celltype) + +se.t = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes.Rds")) +se.t@meta.data = se.t@meta.data %>% mutate( + celltype = case_when( + grepl("^CD4", celltype) & CellCycle == T ~ "CD4.Cycling", + grepl("^CD8", celltype) & CellCycle == T ~ "CD8.Cycling", + celltype == "CD8" ~ "CD8.other", + celltype == "CD4" ~ "CD4.other", + TRUE ~ celltype + ) +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (nonCR vs CR) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# PBMC | Pre | nonCR vs. CR +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj = subset(obj, subset = GROUP == "Pre-infusion") +obj@meta.data = droplevels(obj@meta.data) + +res.pb.pre = run_wilx( + obj = obj, target = "celltype", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.pb.pre.sign = subset(res.pb.pre, significant == T) +ct.keep = names(table(res.pb.pre.sign$cluster)[table(res.pb.pre.sign$cluster) > 10]) +res.pb.pre.sign = res.pb.pre.sign[res.pb.pre.sign$cluster %in% ct.keep, ] +table(res.pb.pre.sign$cluster) + +dgea.pb.pre.pl = dgea_plot(dgea.res = res.pb.pre, dgea.res.sign = res.pb.pre.sign) + +# PBMC | Post | nonCR vs. CR +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj = subset(obj, subset = GROUP == "Post-infusion") +obj@meta.data = droplevels(obj@meta.data) + +res.pb.post = run_wilx( + obj = obj, target = "celltype", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.pb.post.sign = subset(res.pb.post, significant == T) +ct.keep = names(table(res.pb.post.sign$cluster)[table(res.pb.post.sign$cluster) > 10]) +res.pb.post.sign = res.pb.post.sign[res.pb.post.sign$cluster %in% ct.keep, ] +table(res.pb.post.sign$cluster) + +dgea.pb.post.pl = dgea_plot(dgea.res = res.pb.post, dgea.res.sign = res.pb.post.sign) + +# BMMC | Post | nonCR vs. CR +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "BMMC") +obj@meta.data = droplevels(obj@meta.data) + +res.bm.post = run_wilx( + obj = obj, target = "celltype", min.cells = 50, + contrast.group = "RESPONSE_CONSENSUS", contrast = c("nonCR", "CR") +) + +res.bm.post.sign = subset(res.bm.post, significant == T) +ct.keep = names(table(res.bm.post.sign$cluster)[table(res.bm.post.sign$cluster) > 10]) +res.bm.post.sign = res.bm.post.sign[res.bm.post.sign$cluster %in% ct.keep, ] +table(res.bm.post.sign$cluster) + +dgea.bm.post.pl = dgea_plot(dgea.res = res.bm.post, dgea.res.sign = res.bm.post.sign) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (Post vs. Pre) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# PBMC | CR | Post vs Pre +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj = subset(obj, subset = RESPONSE_CONSENSUS == "CR") +obj@meta.data = droplevels(obj@meta.data) + +res.pb.cr = run_wilx( + obj = obj, target = "celltype", min.cells = 50, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +res.pb.cr.sign = subset(res.pb.cr, significant == T) +ct.keep = names(table(res.pb.cr.sign$cluster)[table(res.pb.cr.sign$cluster) > 10]) +res.pb.cr.sign = res.pb.cr.sign[res.pb.cr.sign$cluster %in% ct.keep, ] +table(res.pb.cr.sign$cluster) + +dgea.cr.pl = dgea_plot(dgea.res = res.pb.cr, dgea.res.sign = res.pb.cr.sign) + +# PBMC | nonCR | Post vs Pre +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj = subset(obj, subset = RESPONSE_CONSENSUS == "nonCR") +obj@meta.data = droplevels(obj@meta.data) + +res.pb.noncr = run_wilx( + obj = obj, target = "celltype", min.cells = 50, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +res.pb.noncr.sign = subset(res.pb.noncr, significant == T) +ct.keep = names(table(res.pb.noncr.sign$cluster)[table(res.pb.noncr.sign$cluster) > 10]) +res.pb.noncr.sign = res.pb.noncr.sign[res.pb.noncr.sign$cluster %in% ct.keep, ] +table(res.pb.noncr.sign$cluster) + +dgea.noncr.pl = dgea_plot(dgea.res = res.pb.noncr, dgea.res.sign = res.pb.noncr.sign) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA; write to xlxs +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +write_to_xlsx(res.pb.pre.sign, sheet = "pre PB; T-cell; nonCR vs CR", filename = "code/tables/Supplementaltable4.xlsx") +wb = loadWorkbook("code/tables/Supplementaltable4.xlsx") +write_to_xlsx(df = res.pb.post.sign, filename = "code/tables/Supplementaltable4.xlsx", sheet = "post PB; T-cell; nonCR vs CR", wb = wb) +write_to_xlsx(df = res.bm.post.sign, filename = "code/tables/Supplementaltable4.xlsx", sheet = "post BM; T-cell; nonCR vs CR", wb = wb) +write_to_xlsx(df = res.pb.cr.sign, filename = "code/tables/Supplementaltable4.xlsx", sheet = "CR; PB; T-cell; post vs pre", wb = wb) +write_to_xlsx(df = res.pb.noncr.sign, filename = "code/tables/Supplementaltable4.xlsx", sheet = "nonCR; PB; T-cell; post vs pre", wb = wb) + +dgea.pb.pre.pl = dgea.pb.pre.pl + + ggtitle("\nDEG in pre-infusional T cells population from PBMCs comparing nonCR with CR\n") + + theme(plot.title = element_text(hjust = 0.5, size = rel(1.2), face = "bold")) +dgea.pb.post.pl = dgea.pb.post.pl + + ggtitle("\nDEG in post-infusional T cells population from PBMCs comparing nonCR with CR\n") + + theme(plot.title = element_text(hjust = 0.5, size = rel(1.2), face = "bold")) +dgea.bm.post.pl = dgea.bm.post.pl + + ggtitle("\nDEG in post-infusional T cells population from BMMCs comparing nonCR with CR\n") + + theme(plot.title = element_text(hjust = 0.5, size = rel(1.2), face = "bold")) +dgea.cr.pl = dgea.cr.pl + + ggtitle("\nDEG in T cells from CR comparing post- with pre-infusional PBMC\n") + + theme(plot.title = element_text(hjust = 0.5, size = rel(1.2), face = "bold")) +dgea.noncr.pl = dgea.noncr.pl + + ggtitle("\nDEG in T cells from nonCR comparing post- with pre-infusional PBMC\n") + + theme(plot.title = element_text(hjust = 0.5, size = rel(1.2), face = "bold")) + +ggsave2( + filename="code/figures/supplement/tcell_dgea.pdf", + plot = plot_grid( + dgea.pb.pre.pl, + dgea.pb.post.pl, + dgea.bm.post.pl, + dgea.cr.pl, + dgea.noncr.pl, + ncol = 1, scale = .98, + labels = c("a", "b", "c", "d", "e"), label_fontface = "bold", label_size = 11 + ), + bg = "white", + width = 180, height = 230, units = "mm", scale = 1.6 +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ORA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ora.pb.pre = run_ora(df = res.pb.pre, df.sign = res.pb.pre.sign) +# ora.pb.post = run_ora(res.pb.post, res.pb.post.sign) +# ora.bm.post = run_ora(res.bm.post, res.bm.post.sign) + +# nonCR vs CR +df.ora.1 = rbind( + res.pb.pre.sign %>% mutate(SOURCE = "PBMC_pre"), + res.pb.post.sign %>% mutate(SOURCE = "PBMC_post"), + res.bm.post.sign %>% mutate(SOURCE = "BMMC_post") +) +eg = bitr(res.pb.pre$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +df.ora.1$ENTREZID = eg$ENTREZID[match(df.ora.1$feature, eg$SYMBOL)] +df.ora.1 = df.ora.1[!is.na(df.ora.1$ENTREZID), ] + +df.ora.1$cluster = gsub("\\.", "_", df.ora.1$cluster) +df.ora.1$ID = paste0(df.ora.1$cluster, ".", df.ora.1$SOURCE) +geneList.1 = lapply(split(df.ora.1, df.ora.1$ID), function(x){ + setNames(x$ENTREZID, x$logFC) +}) + +universe = bitr(res.pb.pre$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +universe = universe[!is.na(universe$ENTREZID), ]$ENTREZID + +ora.cc.1 <- compareCluster( + ENTREZID ~ cluster + SOURCE, + data = df.ora.1, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) +) +ora.cc.1 = simplify(ora.cc.1) + +ora.pl.1 = + ora_dotplot_cc( + ora.res = ora.cc.1, + genelist = geneList.1, + order.by.p = F, + nbr.tops = 5, + term.length = 65, + gg.title = "Enrichment test for DEGs comparing nonCR with CR", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = T, + facet.order = c(2, 3), + facet.lvls = c("PBMC_pre", "PBMC_post", "BMMC_post"), + dot.range = c(2, 6) + ) + + scale_size(range = c(2, 5)) + + theme( + # legend.position = "bottom", + axis.text.y = element_text(size = 7, lineheight = .75), + legend.spacing.x = unit(0, 'mm'), + plot.title = element_text(hjust = 0.5, face = "plain", colour = "black", size = 10), + legend.title = element_text(margin = margin(r = 2.5)) + ) + + facet_grid( + ~ facet, scales = "free_x", space = "free", + labeller = labeller(facet = setNames(c("BMMC | post", "PBMC | post", "PBMC | pre"), c("BMMC_post", "PBMC_post", "PBMC_pre"))) + ) + +# Post vs Pre +df.ora.2 = rbind( + res.pb.cr.sign %>% mutate(SOURCE = "PBMC_CR"), + res.pb.noncr.sign %>% mutate(SOURCE = "PBMC_nonCR") +) +eg = bitr(res.pb.cr$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +df.ora.2$ENTREZID = eg$ENTREZID[match(df.ora.2$feature, eg$SYMBOL)] +df.ora.2 = df.ora.2[!is.na(df.ora.2$ENTREZID), ] + +df.ora.2$cluster = gsub("\\.", "_", df.ora.2$cluster) +df.ora.2$ID = paste0(df.ora.2$cluster, ".", df.ora.2$SOURCE) +geneList.2 = lapply(split(df.ora.2, df.ora.2$ID), function(x){ + setNames(x$ENTREZID, x$logFC) +}) + +universe = bitr(res.pb.cr$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +universe = universe[!is.na(universe$ENTREZID), ]$ENTREZID + +ora.cc.2 <- compareCluster( + ENTREZID ~ cluster + SOURCE, + data = df.ora.2, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) +) +ora.cc.2 = simplify(ora.cc.2) + +ora.pl.2 = + ora_dotplot_cc( + ora.res = ora.cc.2, + genelist = geneList.2, + order.by.p = F, + nbr.tops = 5, + term.length = 72, + gg.title = "Enrichment test for DEGs comparing post- with pre-infusional T cell populations", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = T, + facet.order = c(2, 3), + # facet.lvls = c("PBMC_pre", "PBMC_post", "BMMC_post"), + dot.range = c(2, 6) + ) + + theme( + # legend.position = "bottom", + axis.text.y = element_text(size = 8, lineheight = .75), + legend.spacing.x = unit(0, 'mm'), + plot.title = element_text(hjust = 0.5, face = "plain", colour = "black", size = 10), + legend.title = element_text(margin = margin(r = 2.5)) + ) + + facet_grid( + ~ facet, scales = "free_x", space = "free", + labeller = labeller(facet = setNames(c("PBMC | CR", "PBMC | nonCR"), c("PBMC_CR", "PBMC_nonCR"))) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# T-Cell composition (nonCR vs CR) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +p.data = se.meta@meta.data +t = "celltype" +p.data$GROUP_ID = paste0(p.data$TISSUE_SOURCE, " | ", p.data$GROUP) +p.data$GROUP_ID = factor(p.data$GROUP_ID) +# p.data = p.data[p.data$TISSUE_SOURCE != "BMMC", ] +# p.data = p.data[p.data$RESPONSE_CONSENSUS != "CR", ] +p.data = droplevels(p.data) + +comp.pl = + sc_ct_sample_fraction( + inpMeta = p.data, + label = t, + group.facet = "GROUP_ID", + group.color = "RESPONSE_CONSENSUS", + dot.color = "PRODUCT", + nbr.cell.cut = 50, + order.by.ave.prop = T, + group.psz = 1, + filter.ct = "CD4|CD8|gdT" + ) + + scale_y_continuous( + trans = scales::pseudo_log_trans(sigma = 0.0001, base =10), + breaks=c(0, 0.001, 0.01, 0.1, 1) + ) + + coord_cartesian(ylim=c(0, 2)) + +# Speckle +df = droplevels(subset(p.data, GROUP_ID == "PBMC | Pre-infusion")) +res.1 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$RESPONSE_CONSENSUS, transform = "asin" +) %>% mutate(Group = "PBMC | Pre-infusion") + +df = droplevels(subset(p.data, GROUP_ID == "PBMC | Post-infusion")) +res.2 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$RESPONSE_CONSENSUS, transform = "asin" +) %>% mutate(Group = "PBMC | Post-infusion") + +df = droplevels(subset(p.data, GROUP_ID == "BMMC | Post-infusion")) +res.3 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$RESPONSE_CONSENSUS, transform = "asin" +) %>% mutate(Group = "BMMC | Post-infusion") + +res.speckle = rbind(res.1, res.2, res.3) +res.speckle$FILTER = paste0(res.speckle$Group, res.speckle$BaselineProp.clusters) +res.speckle = res.speckle[res.speckle$FILTER %in% paste0(comp.pl$data$group_facet, comp.pl$data$celltype), ] + +res.speckle = res.speckle %>% mutate( + FC = case_when( + PropMean.nonCR > PropMean.CR ~ log2( (PropMean.nonCR / PropMean.CR) ), + PropMean.nonCR < PropMean.CR ~ -log2( (PropMean.CR / PropMean.nonCR) ) + ) +) +res.speckle$LABEL = res.speckle$BaselineProp.clusters +res.speckle$LABEL = ifelse(abs(res.speckle$FC) < log2(1.5), NA, as.character(res.speckle$LABEL)) + +g <- make_gradient( + deg = 180, n = 500, + cols = scico(9, palette = 'vik', begin = .3, end = .7, direction = -1) +) + +lvls = c("PBMC | Pre-infusion", "PBMC | Post-infusion", "BMMC | Post-infusion") +res.speckle$Group = factor(res.speckle$Group, levels = lvls) +t.comp.pl.1 = +ggplot(res.speckle, aes(FC, -log10(P.Value), label = LABEL)) + + annotation_custom( + grob = g, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf + ) + + geom_point(size = .5) + + facet_wrap(~ Group, ncol = 1) + + geom_hline(yintercept = -log10(0.1), linetype = "dashed", lwd = .3) + + geom_vline(xintercept = 0, linetype = "dashed", lwd = .3) + + xlim(-max(abs(res.speckle$FC)), max(abs(res.speckle$FC))) + + xlab("Log2 fold change\nnonCR vs. CR") + + ylab("-log10(p-value)") + + scale_y_continuous(breaks = pretty_breaks(3)) + + annotate("text", x = max(abs(res.speckle$FC)) - .7, y = -log10(.075), label = "p = 0.1", size = 2.5) + + mytheme(base_size = 8) + + theme(panel.spacing = unit(.75, "lines")) + + geom_text_repel( + size = 2.2, + segment.size = .2, + box.padding = 0.4, + # label.padding = .15, + min.segment.length = 0, + max.overlaps = 50 + ) + +# res.speckle = res.speckle[dat$celltype, ] +# res.speckle$yloc = max(comp.pl$data$perc_sample) + .75 +# res.speckle = add_signif(res.speckle,"P.Value", "pval_star", pval.relax = T) +# colnames(res.speckle)[1] = "celltype" +# res.speckle = droplevels(res.speckle) +# +# comp.pl + +# geom_text(data = res.speckle, aes(y = yloc, label = pval_star), size = 2, position = position_dodge(width = .75)) + +# guides(fill = guide_legend(title = NULL, order = 1)) + +# guides(color = guide_legend(title = NULL, override.aes = list(shape = 16, size = 2.5))) + +# theme( +# plot.title = element_text(size = title.size, hjust = 0.5, face = "plain"), +# legend.position = "right" +# ) + +# scale_color_manual(values = c("#994455", "black")) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# T-Cell composition (Post vs Pre) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +p.data = se.meta@meta.data +p.data = p.data[p.data$TISSUE_SOURCE != "BMMC", ] +t = "celltype" +p.data$GROUP_ID = factor(p.data$RESPONSE_CONSENSUS) +p.data = droplevels(p.data) + +comp.pl = + sc_ct_sample_fraction( + inpMeta = p.data, + label = t, + group.facet = "GROUP_ID", + group.color = "GROUP", + dot.color = "PRODUCT", + nbr.cell.cut = 50, + order.by.ave.prop = T, + group.psz = 1, + filter.ct = "CD4|CD8|gdT" + ) + + scale_y_continuous( + trans = scales::pseudo_log_trans(sigma = 0.0001, base =10), + breaks=c(0, 0.001, 0.01, 0.1, 1) + ) + + coord_cartesian(ylim=c(0, 2)) + +# Speckle +df = droplevels(subset(p.data, GROUP_ID == "CR")) +res.1 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$GROUP, transform = "asin" +) %>% mutate(Group = "CR") + +df = droplevels(subset(p.data, GROUP_ID == "nonCR")) +res.2 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$GROUP, transform = "asin" +) %>% mutate(Group = "nonCR") + +res.speckle = rbind(res.1, res.2) +res.speckle$FILTER = paste0(res.speckle$Group, res.speckle$BaselineProp.clusters) +res.speckle = res.speckle[res.speckle$FILTER %in% paste0(comp.pl$data$group_facet, comp.pl$data$celltype), ] + +res.speckle = res.speckle %>% mutate( + FC = case_when( + PropMean.Post.infusion > PropMean.Pre.infusion ~ log2( (PropMean.Post.infusion / PropMean.Pre.infusion) ), + PropMean.Post.infusion < PropMean.Pre.infusion ~ -log2( (PropMean.Pre.infusion / PropMean.Post.infusion) ) + ) +) +res.speckle$LABEL = res.speckle$BaselineProp.clusters +res.speckle$LABEL = ifelse(abs(res.speckle$FC) < log2(1.5), NA, as.character(res.speckle$LABEL)) + +g <- make_gradient( + deg = 180, n = 500, + cols = scico(9, palette = 'vik', begin = .3, end = .7, direction = -1) +) + +t.comp.pl.2 = +ggplot(res.speckle, aes(FC, -log10(P.Value), label = LABEL)) + + annotation_custom( + grob = g, xmin = -Inf, xmax = Inf, ymin = -Inf, ymax = Inf + ) + + geom_point(size = .5) + + facet_wrap(~ Group, ncol = 2) + + geom_hline(yintercept = -log10(0.1), linetype = "dashed", lwd = .3) + + geom_vline(xintercept = 0, linetype = "dashed", lwd = .3) + + xlim(-max(abs(res.speckle$FC)), max(abs(res.speckle$FC))) + + xlab("Log2 fold change\npost vs. pre") + + ylab("-log10(p-value)") + + scale_y_continuous(breaks = pretty_breaks(3)) + + annotate("text", x = -max(abs(res.speckle$FC)) + .2, y = -log10(.08), label = "p = 0.1", size = 3) + + mytheme(base_size = 8) + + theme(panel.spacing = unit(1, "lines")) + + geom_text_repel( + size = 2.3, + segment.size = .2, + min.segment.length = 0, + max.overlaps = 50 + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Patient 12: Post infusion CAR T vs premanufacture T cells in PBMC +# CD4 and CD8 +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (RNA) +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj.p12 = subset(obj, subset = PATIENT_ID == "Patient 012") +m = FetchData(obj.p12, vars = c("GROUP", "CAR_BY_EXPRS")) +rm.cells = rownames(m[m$GROUP == "Post-infusion" & m$CAR_BY_EXPRS == F, ]) +obj.p12 = obj.p12[, !colnames(obj.p12) %in% rm.cells] +obj.p12@meta.data = droplevels(obj.p12@meta.data) + +res.p12.lin = run_wilx( + obj = obj.p12, target = "celltype_short_3", min.cells = 10, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) +res.p12.lin.sign = subset(res.p12.lin, significant == T) +# UpSet(make_comb_mat(lapply(split(res.p12.lin.sign, res.p12.lin.sign$cluster), function(x){x$feature}))) + +volcano.p12 = + dgea_volcano( + dgea.res = res.p12.lin, + dgea.res.sign = res.p12.lin.sign, + sort.by = "padj", + nbr.tops = 10, + nudge_x = 3, + cl.label = setNames(c("CD4", "CD8"), c("CD4 T-Cell", "CD8 T-Cell")) +) + +# DGEA (ADT) +adt.wilx.p12 = run_wilx( + obj = obj.p12, target = "celltype_short_3", min.cells = 10, assay = "ADT", + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +adt.wilx.p12$feature_2 = adt.rna.mapping[match(adt.wilx.p12$feature, names(adt.rna.mapping))] +adt.wilx.p12 = adt.wilx.p12[adt.wilx.p12$significant == T, ] + +obj.p12$GROUP_C = ifelse(obj.p12$GROUP == "Pre-infusion", "pre T", "CAR T") + +adt.wilx.p12.sub = adt.wilx.p12[adt.wilx.p12$cluster == "CD4 T-Cell", ] +marker.adt.cd4.p12 = + plot_marker( + obj = obj.p12[, obj.p12$celltype_short_3 == "CD4 T-Cell"], + wlx.sign = adt.wilx.p12.sub, + ftr.labels = setNames(adt.wilx.p12.sub$feature_2, adt.wilx.p12.sub$feature), + grp = "GROUP_C" + ) + ggtitle("CD4") + +adt.wilx.p12.sub = adt.wilx.p12[adt.wilx.p12$cluster == "CD8 T-Cell", ] +marker.adt.cd8.p12 = plot_marker( + obj = obj.p12[, obj.p12$celltype_short_3 == "CD8 T-Cell"], + wlx.sign = adt.wilx.p12.sub, + ftr.labels = setNames(adt.wilx.p12.sub$feature_2, adt.wilx.p12.sub$feature), + grp = "GROUP_C" +) + ggtitle("CD8") + +# ORA +ora.cc.p12 = run_ora(df = res.p12.lin, df.sign = res.p12.lin.sign) +ora.pl.p12 = + ora_dotplot_cc( + ora.res = ora.cc.p12$ore.res, + genelist = ora.cc.p12$geneList, + order.by.p = F, + nbr.tops = 15, + term.length = 52, + gg.title = "", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = F, + facet.order = c(2), + dot.range = c(2, 6) +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Patient 12: Post infusion CAR T vs premanufacture T cells in PBMC +# All T cell populations +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (RNA) +res.p12.all = run_wilx( + obj = obj.p12, target = "celltype", min.cells = 10, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) +res.p12.all.sign = subset(res.p12.all, significant == T) + +# volcano.p12.all = +# dgea_volcano( +# dgea.res = res.p12.all, +# dgea.res.sign = res.p12.all.sign, +# sort.by = "padj", +# nbr.tops = 10, +# nudge_x = 3.5 +# ) + +# ORA +ora.cc.p12.all = run_ora(df = res.p12.all, df.sign = res.p12.all.sign) +ora.pl.p12.all = + ora_dotplot_cc( + ora.res = ora.cc.p12.all$ore.res, + genelist = ora.cc.p12.all$geneList, + order.by.p = F, + nbr.tops = 10, + term.length = 100, + gg.title = "Patient 12", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = F, + facet.order = c(2), + dot.range = c(2, 6) + ) + +ggsave2( + filename="code/figures/supplement/tcell_dgea_p12_CAR_vs_pre.pdf", + plot = plot_grid( + plot_grid(NULL, ora.pl.p12.all, rel_widths = c(.04, 1)) + ), bg = "white", + width = 180, height = 180, units = "mm", scale = 1.4 +) + + +wb = loadWorkbook("code/tables/Supplementaltable4.xlsx") +write_to_xlsx( + df = rbind( + res.p12.lin.sign, + res.p12.all.sign + ), + filename = "code/tables/Supplementaltable4.xlsx", + sheet = "P12; CAR vs pre T-cell", wb = wb +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA +# Patient 14: Post infusion CAR T vs premanufacture T cells in PBMC +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (RNA) +obj = se.t +obj = subset(obj, subset = TISSUE_SOURCE == "PBMC") +obj.p14 = subset(obj, subset = PATIENT_ID == "Patient 014") +m = FetchData(obj.p14, vars = c("GROUP", "CAR_BY_EXPRS")) +rm.cells = rownames(m[m$GROUP == "Post-infusion" & m$CAR_BY_EXPRS == F, ]) +obj.p14 = obj.p14[, !colnames(obj.p14) %in% rm.cells] +obj.p14@meta.data = droplevels(obj.p14@meta.data) +obj.p14$tmp = "T-cell" + +res.p14.lin = run_wilx( + obj = obj.p14, target = "celltype_short_3", min.cells = 10, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) +res.p14.lin.sign = subset(res.p14.lin, significant == T) +# UpSet(make_comb_mat(lapply(split(res.p14.lin.sign, res.p14.lin.sign$cluster), function(x){x$feature}))) + +volcano.p14 = dgea_volcano( + dgea.res = res.p14.lin, + dgea.res.sign = res.p14.lin.sign, + sort.by = "padj", + nbr.tops = 10, + nudge_x = 3, + cl.label = setNames(c("CD4", "CD8"), c("CD4 T-Cell", "CD8 T-Cell")) +) + +# DGEA (ADT) +adt.wilx.p14 = run_wilx( + obj = obj.p14, target = "celltype_short_3", min.cells = 10, assay = "ADT", + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +adt.wilx.p14$feature_2 = adt.rna.mapping[match(adt.wilx.p14$feature, names(adt.rna.mapping))] +adt.wilx.p14 = adt.wilx.p14[adt.wilx.p14$significant == T, ] + +obj.p14$GROUP_C = ifelse(obj.p14$GROUP == "Pre-infusion", "pre T", "CAR T") +adt.wilx.p14.sub = adt.wilx.p14[adt.wilx.p14$cluster == "CD8 T-Cell", ] +marker.adt.cd8.p14 = +plot_marker( + obj = obj.p14[, obj.p14$celltype_short_3 == "CD8 T-Cell"], + wlx.sign = adt.wilx.p14.sub[!adt.wilx.p14.sub$feature %in% c("CD11B", "CCR10"), ], + ftr.labels = setNames(adt.wilx.p14.sub$feature_2, adt.wilx.p14.sub$feature), + grp = "GROUP_C" +) + ggtitle("CD8") + +adt.wilx.p14.sub = adt.wilx.p14[adt.wilx.p14$cluster == "CD4 T-Cell", ] +marker.adt.cd4.p14 = + plot_marker( + obj = obj.p14[, obj.p14$celltype_short_3 == "CD4 T-Cell"], + wlx.sign = adt.wilx.p14.sub %>% arrange(-abs(logFC)) %>% slice_head(n=12), + ftr.labels = setNames(adt.wilx.p14.sub$feature_2, adt.wilx.p14.sub$feature), + grp = "GROUP_C" +) + + ggtitle("CD4") + + theme(legend.position = "bottom", legend.key.size = unit(.3, 'cm')) + +# ORA +ora.cc.p14 = run_ora(df = res.p14.lin, df.sign = res.p14.lin.sign) +ora.pl.p14 = + ora_dotplot_cc( + ora.res = ora.cc.p14$ore.res, + genelist = ora.cc.p14$geneList, + order.by.p = F, + nbr.tops = 15, + term.length = 52, + gg.title = "", + base.size = 8, + min.size = 5, + quantile.cut = T, + x.text.size = rel(1), + facet = F, + facet.order = c(2), + dot.range = c(2, 6) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Patient 14: Post infusion CAR T vs premanufacture T cells in PBMC +# All T cell populations +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA (RNA) +res.p14.all = run_wilx( + obj = obj.p14, target = "celltype", min.cells = 10, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) +res.p14.all.sign = subset(res.p14.all, significant == T) + +# volcano.p14.all = +# dgea_volcano( +# dgea.res = res.p14.all, +# dgea.res.sign = res.p14.all.sign, +# sort.by = "padj", +# nbr.tops = 10, +# nudge_x = 2 +# ) + +wb = loadWorkbook("code/tables/Supplementaltable4.xlsx") +write_to_xlsx( + df = rbind( + res.p14.lin.sign, + res.p14.all.sign + ), + filename = "code/tables/Supplementaltable4.xlsx", + sheet = "P14; CAR vs pre T-cell", wb = wb +) + +# # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# # DGEA +# # Patient 7,8: BM cv PB +# # >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# obj = se.t +# obj.p7.p8 = subset(obj, subset = PATIENT_ID == "Patient 007" |PATIENT_ID == "Patient 008") +# obj.p7.p8.post = subset(obj.p7.p8, GROUP == "Post-infusion") +# +# res.obj.p7.p8.post = run_wilx( +# obj = obj.p7.p8.post, target = "celltype_short_3", min.cells = 10, +# contrast.group = "TISSUE_SOURCE", contrast = c("PBMC", "BMMC") +# ) +# res.obj.p7.p8.post.sign = subset(res.obj.p7.p8.post, significant == T) +# table(res.obj.p7.p8.post.sign$cluster) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +deg.rna.p12 = plot_grid( + ggdraw() + + draw_label( + "DEG - CAR vs. pre-infusional T cells in P12", + fontface = 'plain', hjust = 0.5, size = 10 + ), + volcano.p12 + theme(panel.spacing = unit(2, "lines")), + nrow = 2, rel_heights = c(.1, 1) + ) + +deg.adt.p12 = plot_grid( + ggdraw() + + draw_label( + "Differentially expressed surface antigens in P12", + fontface = 'plain', hjust = 0.5, size = 10 + ), + plot_grid( + marker.adt.cd4.p12 + theme(legend.position = "none"), + NULL, + marker.adt.cd8.p12 + theme(legend.position = "none"), + ncol = 3, rel_widths = c(1, .1, 1) + ), + get_legend(marker.adt.cd4.p14), + nrow = 3, rel_heights = c(.1, 1, .1) +) + +deg.rna.p14 = plot_grid( + ggdraw() + + draw_label( + "DEG - CAR vs. pre-infusional T cells in P14", + fontface = 'plain', hjust = 0.5, size = 10 + ), + volcano.p14, + nrow = 2, rel_heights = c(.1, 1) +) + +deg.adt.p14 = plot_grid( + ggdraw() + + draw_label( + "Differentially expressed surface antigens in P14", + fontface = 'plain', hjust = 0.5, size = 10 + ), + plot_grid( + marker.adt.cd4.p14 + theme(legend.position = "none"), + NULL, + marker.adt.cd8.p14 + theme(legend.position = "none"), + ncol = 3, rel_widths = c(1, .1, 1) + ), + get_legend(marker.adt.cd4.p14), + nrow = 3, rel_heights = c(.1, 1, .1) +) + +ggsave2( + filename="code/figures/main/figure_07.pdf", + plot_grid( + plot_grid( + t.comp.pl.1, NULL, ora.pl.1, + ncol = 3, rel_widths = c(1, .3, 3), + labels = c("a", "", "b"), label_fontface = "bold", label_size = 11 + ), + # NULL, + NULL, + plot_grid( + plot_grid( + deg.rna.p12, + NULL, + deg.adt.p12, + nrow = 3, rel_heights = c(1, .075, 1), + labels = c("c", "", "d"), label_fontface = "bold", label_size = 11 + ), + NULL, + plot_grid( + deg.rna.p14, + NULL, + deg.adt.p14, + nrow = 3, rel_heights = c(1, .05, 1), + labels = c("e", "", "f"), label_fontface = "bold", label_size = 11 + ), + ncol = 3, rel_widths = c(1, .125, 1) + ), + nrow = 3, rel_heights = c(2.5, .1, 2.5) + ), + width = 180, + height = 230, + dpi = 300, + # bg = NULL, + bg = "white", + units = "mm", + scale = 1.6 +) + +ggsave2( + filename="code/figures/supplement/tcell_comp_post_vs_pre.pdf", + plot = plot_grid( + plot_grid(NULL, t.comp.pl.2, NULL, nrow = 1, rel_widths = c(.2, 1, .2)), + NULL, + plot_grid(NULL, ora.pl.2, rel_widths = c(.04, 1)), + nrow = 3, rel_heights = c(1, .1, 2), + labels = c("a", "", "b"), label_fontface = "bold", label_size = 11 + ), bg = "white", + width = 180, height = 160, units = "mm", scale = 1.6 +) + +ggsave2( + filename="code/figures/supplement/CAR_vs_pre_T_ora.pdf", + plot = plot_grid( + ora.pl.p12 + ggtitle("Patient 12"), + NULL, + ora.pl.p14 + ggtitle("Patient 14"), + ncol = 3, rel_widths = c(1, .1, 1), align = "vh", + labels = c("a", "", "b"), label_fontface = "bold", label_size = 11 + ), + width = 180, height = 160, units = "mm", scale = 1.4 +) + + diff --git a/code/figure_scripts/figure_08.R b/code/figure_scripts/figure_08.R new file mode 100644 index 0000000..ff5dfb2 --- /dev/null +++ b/code/figure_scripts/figure_08.R @@ -0,0 +1,457 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "ggalluvial", + "rlang", "remotes", "patchwork", "cowplot", "ggrepel", "scico", "scattermore", + "circlize", "purrr", "readr", "openxlsx" +) +.bioc_packages = c( + "SummarizedExperiment", "scRepertoire", "infercnv", "ComplexHeatmap", "GenomicRanges" +) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +source("code/helper/styles.R") +source("code/helper/functions_plots.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) + +grie.p1.b = readRDS(paste0(manifest$meta$work, "integration/grieb_p001_bclones.Rds")) +grie.t = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes.Rds")) +dara.t = readRDS(paste0(manifest$dara$work, "seurat_clonotypes_t.Rds")) +dara.t = dara.t[, !is.na(dara.t$CTstrict)] + +seuratobj_dara = readRDS( + paste0(manifest$base$workdata, "singleron/infercnv/MERZ001/seuratobj.rds") +) +splitted = readRDS(paste0(manifest$meta$work, '/infercnv/patient1_clusterlevel/splitted.Rds')) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# BCMA, CD38, SLAMF7 expression +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +se.pre = se.meta[, se.meta$TISSUE_SOURCE == 'PBMC'& se.meta$GROUP == 'Pre-infusion'] +pl_l_pre = plot_marker( + obj = se.pre, + ftrs = c('CD38', 'CD269', 'CD319'), + col = "celltype", + col.lvl = "Plasmablast", + label = "Pre" +) + +se.post = se.meta[, se.meta$TISSUE_SOURCE == 'PBMC'& se.meta$GROUP == 'Post-infusion'] +pl_l_post = plot_marker( + obj = se.post, + ftrs = c('CD38', 'CD269', 'CD319'), + col = "celltype", + col.lvl = "Plasmablast", + label = "Post" +) + +a.b.c = + plot_grid( + plot_grid( + ggdraw() + + draw_label( + "BCMA expression on circulating PC", + fontface = 'plain', hjust = 0.5, size = 10 #x = 0, + ), + plot_grid( + pl_l_pre$CD269 + theme(legend.position = "none"), + NULL, + pl_l_post$CD269 + theme(legend.position = "none"), + ncol = 3, rel_widths = c(1.618, .1, 1) + ), + nrow = 2, rel_heights = c(.1, 1) + ), + NULL, + plot_grid( + ggdraw() + + draw_label( + "CD38 expression on circulating PC", + fontface = 'plain', hjust = 0.5, size = 10 #x = 0, + ), + plot_grid( + pl_l_pre$CD38 + theme(legend.position = "none"), + NULL, + pl_l_post$CD38 + theme(legend.position = "none"), + ncol = 3, rel_widths = c(1.618, .1, 1) + ), + nrow = 2, rel_heights = c(.1, 1) + ), + NULL, + plot_grid( + ggdraw() + + draw_label( + "SLAMF7 expression on circulating PC", + fontface = 'plain', hjust = 0.5, size = 10 #x = 0, + ), + plot_grid( + pl_l_pre$CD319 + theme(legend.position = "none"), + NULL, + pl_l_post$CD319 + theme(legend.position = "none"), + ncol = 3, rel_widths = c(1.618, .1, 1) + ), + nrow = 2, rel_heights = c(.1, 1) + ), + nrow = 5, rel_heights = c(1, .1, 1, .1, 1), + labels = c("a", "", "b", "", "c"), label_fontface = "bold", label_size = 11, vjust = 1.2 +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# P01: cytoband +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Function to find bands corresponding to CNV hit ranges +find_bands=function(cinnie_grange){ + + overlap_hit=findOverlaps(cinnie_grange, cytoband_grange) + overlaps <- pintersect(cinnie_grange[queryHits(overlap_hit)], cytoband_grange[subjectHits(overlap_hit)]) + overlaps_df=as.data.frame(cinnie_grange[queryHits(overlap_hit)]) %>% unite(range, seqnames, start, sep=":", remove=F) %>% unite(range, range, end, sep="-", remove=F) %>% select(range:end,range, id, orig.ident, state, prob) + overlaps_df$percentOverlap <- width(overlaps) / width(cytoband_grange[subjectHits(overlap_hit)]) > 0.5 + overlaps_df$band=cytoband_grange$band[subjectHits(overlap_hit)] + overlaps_df=overlaps_df[overlaps_df$percentOverlap,] #Keep only those with 0.5 overlap + + overlaps_df$percentOverlap=as.integer(overlaps_df$percentOverlap) #Convert to integer to calculate jaccard later on + + overlaps_df=overlaps_df %>% arrange(seqnames, start) + + return(overlaps_df) +} + +#Make cytoband grange +cytoband = readr::read_tsv('data/cytoBand.txt', col_names = c('chr','start','end','band','stain'), show_col_types = FALSE) +cytoband=cytoband %>% filter(!grepl('^chr.{1,2}_',chr)) %>% mutate(chr=gsub('chr','',chr), chr2=chr) %>% unite("band",chr2, band, sep="") #chromosome number to band +cytoband_grange=makeGRangesFromDataFrame(cytoband %>% select(chr:band), keep.extra.columns = T) + +# Function for getting probabilities of CNV regions +gather_results3=function(result_path){ + + tabulate_results=function(i){ + samples_path=file.path(result_path, i) + + cnv_regions_dat=readr::read_tsv(file.path(samples_path, 'HMM_CNV_predictions.HMMi3.hmm_mode-samples.Pnorm_0.5.pred_cnv_regions.dat'), show_col_types = FALSE) + + + MCMC_inferCNV_obj=readRDS(file.path(samples_path, 'BayesNetOutput.HMMi3.hmm_mode-samples/MCMC_inferCNV_obj.rds')) + max_prob=map(MCMC_inferCNV_obj@cnv_probabilities[MCMC_inferCNV_obj@cnv_regions %in% cnv_regions_dat$cnv_name], ~max(apply(.x,2,median))) + cnv_regions_dat$prob=unlist(max_prob) + + status_ind=c('Loss','Neutral','Gain') + cnv_regions_dat$state=status_ind[cnv_regions_dat$state] + cnv_regions_dat=cnv_regions_dat %>% select(-cnv_name) %>% + group_by(cell_group_name) %>% mutate(id=paste0(i, '_cluster_', cur_group_id()), .before=state) %>% + ungroup %>% select(-cell_group_name)} + + samples=list.dirs(result_path, full.names=F, recursive=F) + samples=samples[!grepl("^\\.|heatmaps", samples)] + table_results_= map(samples, tabulate_results) + + table_results=bind_rows(table_results_) + + table_results=table_results %>% filter(state!='Neutral') %>% filter(chr!=23) + return(table_results) + +} + +#Extract CNV probabilities +patient1_results=gather_results3(paste0(manifest$meta$work, '/infercnv/patient1_clusterlevel')) +patient1_results=patient1_results %>% mutate(id=gsub("_cluster_1$", "", id)) + +#Prepare plot for fig 8F +table_cinnie_=patient1_results %>% mutate(orig.ident=str_match(.$id, "(.*)_cluster")[,2]) +cinnie_grange = makeGRangesFromDataFrame(table_cinnie_, keep.extra.columns=T) + +bands_concat=find_bands(cinnie_grange) +bands_concat=bands_concat %>% mutate(state=ifelse(state=='Gain',1,-1)) %>% mutate(prob=prob*state) +bands_concat=bands_concat %>% mutate(seqnames=as.integer(as.character(seqnames))) %>%arrange(seqnames, start) + +bands_concat=bands_concat %>% group_by(orig.ident, state, band) %>% + mutate(gain_check=max(prob)>0.95 & prob>0.8) %>% + mutate(loss_check=min(prob)< -0.95 & prob< -0.8) #Filter CNVs by probabilities +bands_concat_continuous=bands_concat %>% mutate(prob=ifelse(gain_check|loss_check, prob, NA)) + +bands_df_widened=bands_concat_continuous %>% pivot_wider(id_cols=id, names_from=band, values_from=prob, values_fill=NA) %>% + arrange(id) + +mat_for_jaccard=as.matrix(bands_df_widened %>% select(-id)) +rownames(mat_for_jaccard)=bands_df_widened$id + +col_fun_plot = colorRamp2(c(-1, 0, 1), c("blue", "white","red")) + +# Plot Figure 8F. +options(repr.plot.width = 30, repr.plot.height =2) +band_annot=str_extract(colnames(mat_for_jaccard), "^\\d{1,2}(p|q)") +band_annot=factor(band_annot, levels=unique(band_annot)) +levels(band_annot)=str_sort(levels(band_annot), numeric = T) +rownames(mat_for_jaccard) = c("Clone 1", "Clone 2", "Clone 3") +ht1 = + Heatmap( + mat_for_jaccard, + cluster_columns=F, + cluster_rows=F, + show_row_names=T, + show_column_names=F, + show_heatmap_legend = F, + column_split = band_annot, + cluster_row_slices =F, + cluster_column_slices=F, + col=col_fun_plot, + na_col = "white", + row_names_gp = gpar(fontsize = 8), + column_title_gp = gpar(fontsize = 6), + border = TRUE, + column_title_rot = 45, +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# P01: B-clones (Singleron) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Prepare metadata for Dara dataset +project1_metadata = seuratobj_dara@meta.data %>% + dplyr::select(orig.ident, source=CellType, patient, collection.day=date.of.sample.acquisition) %>% + dplyr::mutate(batch=1) %>% + dplyr::mutate(patient = as.integer(str_match(patient, 'Patient_0+(\\d+)')[,2])) + +# Prepare metadata for CART dataset +grie.p1.b=grie.p1.b[, grie.p1.b$CTstrict=='IGH.1.IGHV3-33_IGLC:LD.1.IGLV2-14'] +metadata_excel=read.csv('data/metadata.csv') +metadata_excel = openxlsx::read.xlsx("data/clinicopathological_discovery.xlsx", check.names = F, detectDates = T) + +metadata_excel = metadata_excel %>% select(orig.ident=SAMPLE_NAME, collection.day) +project2_metadata=grie.p1.b@meta.data %>% rownames_to_column('rn') +project2_metadata=project2_metadata %>% select(rn, orig.ident, source=TISSUE_SOURCE, patient=PATIENT_ID) +project2_metadata$patient=1 +project2_metadata=project2_metadata %>% left_join(metadata_excel, by = "orig.ident") %>% + mutate(collection.day=as.character(collection.day)) %>% mutate(batch=2) +project2_metadata=column_to_rownames(project2_metadata, 'rn') + +# Combine metadata +projects_metadata = bind_rows(project1_metadata, project2_metadata) + +# Add metadata to CNV clusters +cnv_clusters_celllevel_combined = merge(data.frame(cnv_cluster=splitted), projects_metadata, by='row.names')%>% + mutate(source=gsub('MC','',source)) %>% + dplyr::rename(date = 'collection.day') + +# Sum up and calculate percentages +cnv_clusters_full = cnv_clusters_celllevel_combined %>% + dplyr::count(cnv_cluster, date, orig.ident, patient, source, batch, name='count') +cnv_clusters_full = cnv_clusters_full %>% + dplyr::group_by(date, patient, source) %>% + dplyr::mutate(total=sum(count)) %>% + dplyr::mutate(percentage=count/total*100) %>% + ungroup +cnv_clusters_full_long = cnv_clusters_full %>% + pivot_longer(c(count, percentage), names_to='freq') %>% + unite(day_source, date, source) %>% + dplyr::rename("donors"="patient","group_id"="cnv_cluster") %>% + dplyr::mutate(group_id=as.character(group_id)) + +lvls = c("UC1E10", "UC1EJZ", "UC2J5Z", "UC2J26", "MXMERZ002A_13", "MXMERZ002A_01", "MXMERZ002A_02") +lvls = setNames(paste0(seq(1, 7), "_", lvls), lvls) +cnv_clusters_full_long$TMP = lvls[match(cnv_clusters_full_long$orig.ident, names(lvls))] + +x.labels = setNames( + c("d 30\nPB", "d -30\nPB", "d 30\nBM", "d 0\nPB", "d 4\nPB", "d 4\nPB", "d 0\nPB"), + c("7_MXMERZ002A_02", "5_MXMERZ002A_13", "6_MXMERZ002A_01", "1_UC1E10", "2_UC1EJZ", "4_UC2J26", "3_UC2J5Z") +) + +b.clones.1.pl = ggplot( + cnv_clusters_full_long[cnv_clusters_full_long$freq == "count", ], + aes( + x = TMP, fill = group_id, group = group_id, stratum = group_id, + alluvium = group_id, y = value, label = group_id) +) + + geom_stratum() + + geom_flow(stat = "alluvium") + + mytheme(base_size = 8) + + theme( + legend.justification=c(1,.9), + legend.position=c(.95, .95), + legend.key.size = unit(.3, 'cm') + ) + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Cells") + + xlab(NULL) + + labs(fill = "Clone") + + scale_x_discrete(labels = x.labels) + + geom_alluvium(color="black", linewidth = .01) + + +b.clones.2.pl = ggplot( + cnv_clusters_full_long[cnv_clusters_full_long$freq != "count", ], + aes( + x = TMP, fill = group_id, group = group_id, stratum = group_id, + alluvium = group_id, y = value, label = group_id) +) + +geom_stratum() + + geom_flow(stat = "alluvium") + + mytheme(base_size = 8) + + theme(legend.position = "none") + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Proportion") + + xlab(NULL) + + scale_x_discrete(labels = x.labels) + + geom_alluvium(color="black", linewidth = .01) + +b.clone.fin = + plot_grid( + ggdraw() + + draw_label( + "Malignant plasma cells P01", + fontface = 'plain', hjust = .4, size = 10, y = .6 + ), + NULL, + plot_grid( + b.clones.1.pl, + NULL, + b.clones.2.pl, + nrow = 3, rel_heights = c(1, .05, 1) + ), + ggdraw() + + draw_label( + "Time point with specific therapy line", + fontface = 'plain', hjust = 0.4, size = 8 + ), + nrow = 4, rel_heights = c(.04, .05, 1, .05), + labels = c("d", "", "", ""), label_fontface = "bold", label_size = 11, vjust = 1.2 + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# P01: T-clones +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +grie.t.p1 = grie.t[, grie.t$PATIENT_ID == "Patient 001"] +grie.t.p1@meta.data = grie.t.p1@meta.data %>% + dplyr::mutate( + TIMEPOINT = dplyr::case_when( + TIMEPOINT == "Pre" ~ "d -30", + TIMEPOINT == "Wk 3 to 4" ~ "d 30" + ) + ) +se.p1.t = merge(grie.t.p1, dara.t) +se.p1.t$TISSUE_SOURCE = ifelse(se.p1.t$TISSUE_SOURCE == "PBMC", "PB", "BM") +lvls = c("UC1E10", "UC1EJZ", "UC2J5Z", "UC2J26", "MXMERZ002A_13", "MXMERZ002A_01", "MXMERZ002A_02") +lvls = setNames(paste0(seq(1, 7), "_", lvls), lvls) +se.p1.t$TMP = lvls[match(se.p1.t$orig.ident, names(lvls))] +se.p1.t$TMP2 = paste0(se.p1.t$TIMEPOINT, "\n", se.p1.t$TISSUE_SOURCE) +x.labels = setNames(se.p1.t$TMP2, se.p1.t$TMP) +x.labels = x.labels[!duplicated(names(x.labels))] + +t.clones.1.pl = + compareClonotypes.cust( + df = se.p1.t, + split.by = "TMP", + numbers = 3, + exportTable = F, + scale = T + ) + + mytheme(base_size = 8) + + theme(legend.position = "none") + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Proportion") + + xlab(NULL) + + scale_x_discrete(labels = x.labels) + + geom_alluvium(color="black", linewidth = .01) + +t.clones.2.pl = + compareClonotypes.cust( + df = se.p1.t, + split.by = "TMP", + numbers = 3, + exportTable = F, + scale = F + ) + + mytheme(base_size = 8) + + theme(legend.position = "none") + + scale_fill_manual(values = colors_use.20) + + scale_y_continuous(breaks = pretty_breaks(n = 3)) + + ylab("Cells") + + xlab(NULL) + + scale_x_discrete(labels = x.labels) + + geom_alluvium(color="black", linewidth = .01) + +t.clone.fin = + plot_grid( + ggdraw() + + draw_label( + "T cells P01", + fontface = 'plain', hjust = 0.3, size = 10, y = .6, + ), + NULL, + plot_grid( + t.clones.2.pl, + NULL, + t.clones.1.pl, + nrow = 3, rel_heights = c(1, .05, 1) + ), + ggdraw() + + draw_label( + "Time point with specific therapy line", + fontface = 'plain', hjust = 0.4, size = 8 + ), + nrow = 4, rel_heights = c(.04, .05, 1, .05), + labels = c("e", "", "", ""), label_fontface = "bold", label_size = 11, vjust = 1.2 + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Plot +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ggsave2( + filename="code/figures/main/figure_08.png", + plot_grid( + plot_grid( + plot_grid(a.b.c, get_legend(pl_l_pre$CD38 + theme(legend.key.size = unit(.4, 'cm'))), rel_widths = c(1, .15)), + NULL, + b.clone.fin, + NULL, + t.clone.fin, + ncol = 5, rel_widths = c(1.9, 0, 1, .1, 1) + ), + NULL, + plot_grid( + ggdraw() + + draw_label( + "BCMA expression on circulating PC", + fontface = 'plain', hjust = 0.5, size = 10 #x = 0, + ), + plot_grid(grid.grabExpr(draw(ht1, merge_legend = TRUE))), + nrow = 2, rel_heights = c(.2, 1) + + ), + nrow = 3, rel_heights = c(1, .025, .15), + labels = c("", "", "f"), label_fontface = "bold", label_size = 11, vjust = 1 + ), + width = 180, height = 120, dpi = 300, bg = "white", units = "mm", scale = 1.6 +) + + diff --git a/code/figure_scripts/figure_08_run_infercnv.R b/code/figure_scripts/figure_08_run_infercnv.R new file mode 100644 index 0000000..3e7460d --- /dev/null +++ b/code/figure_scripts/figure_08_run_infercnv.R @@ -0,0 +1,219 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "ggalluvial", + "rlang", "remotes", "patchwork", "cowplot", "ggrepel", "scico", "parallelDist", + "parallel", "tryCatchLog", "futile.logger", "circlize", "purrr" +) +.bioc_packages = c( + "SummarizedExperiment", "scRepertoire", "infercnv", "ComplexHeatmap", "GenomicRanges" +) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"Azimuth" %in% installed.packages())) { + devtools::install_github('satijalab/seurat-data') +} +library(SeuratData) + +source("code/helper/styles.R") +source("code/helper/functions_plots.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +# se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) + +grie.p1.b = readRDS(paste0(manifest$meta$work, "integration/grieb_p001_bclones.Rds")) + +if(!any(InstalledData()$Dataset %in% "bmcite")){ + InstallData("bmcite") + bm = bmcite +} else { + bm = LoadData(ds = "bmcite") +} +bm=bm[,bm$celltype.l2=='Plasmablast'] +bm=RenameCells(object = bm, add.cell.id = "normcell_") +bm=bm[,bm@meta.data$donor=='batch1'] #Use just one of the 2 batches available + +#Load Dara dataset +seuratobj_dara = readRDS( + paste0(manifest$base$workdata, "singleron/infercnv/MERZ001/seuratobj.rds") +) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +#Subset to patient 1 and cancer cells for dara +seuratobj_dara=seuratobj_dara[, seuratobj_dara$patient %in% c("Patient_001") & seuratobj_dara$B_cell_malignant %in% c('malignant')] + +# Subset to cancer cells +grie.p1.b = grie.p1.b[, grie.p1.b$CTstrict=='IGH.1.IGHV3-33_IGLC:LD.1.IGLV2-14'] + +# Merge dara, michael and normal +intersected_genes = Reduce(intersect, list(rownames(grie.p1.b),rownames(seuratobj_dara),rownames(bm))) +seuratobj_merged = merge( + grie.p1.b[intersected_genes,], + c(seuratobj_dara[intersected_genes,], bm[intersected_genes, ]) +) +seuratobj_merged$patient = 'patient1_bmcite_batch1' + +#Function for identifying normal cells +identify_normal_normcell=function(reference1){ + non_maglinant_cells=colnames(reference1)[grepl("^normcell_", colnames(reference1))] + non_maglinant_cells=na.omit(non_maglinant_cells) + maglinant_cells=colnames(reference1)[!grepl("^normcell_", colnames(reference1))] + maglinant_cells=na.omit(maglinant_cells) + + return(list(maglinant=maglinant_cells, normal=non_maglinant_cells)) +} + +#Function for running inferCNV +get_infercnv=function(reference1, folder, subcluster=NA, pval=NA, groups=NA, analysis_mode='subclusters'){ + #reference1=seuratobj_merged + #pval=0.5 + samplename=reference1$patient[1] + sample_annotation_filename=file.path(folder, paste0(samplename, '_sampleannotation.tsv')) + + infercnv_obj= tryCatchLog( + expr = { + counts_matrix = GetAssayData(reference1, slot="counts", assay='RNA') + + #Make sample annotation file + #################### Identify normal cells function + cell_types=identify_normal_normcell(reference1) + + ####################### + malignant='malignant_plasmacells' + normalcells='normal_plasmacells' + + cellannotation=rbind(data.frame(x=cell_types$maglinant, y=malignant), data.frame(x=cell_types$normal, y=normalcells)) + write.table(cellannotation, sample_annotation_filename, col.names=F, row.names=F, sep='\t') + + + # create the infercnv object + infercnv_obj = CreateInfercnvObject(raw_counts_matrix=counts_matrix, + annotations_file=sample_annotation_filename, + delim="\t", + gene_order_file="data/gene_ordering.tsv", + ref_group_names=normalcells) + + # perform infercnv operations to reveal cnv signal + infercnv_obj = infercnv::run(infercnv_obj, + cutoff=0.1, # use 1 for smart-seq, 0.1 for 10x-genomics + out_dir=file.path(folder,samplename), # dir is auto-created for storing outputs + cluster_by_groups=T, # set to True to avoid https://github.com/broadinstitute/infercnv/issues/526 + denoise=T, + num_threads=8, + HMM_type = 'i3', + analysis_mode=analysis_mode, + hclust_method='ward.D2', + leiden_resolution=pval, + tumor_subcluster_partition_method=subcluster, + HMM=T, + no_prelim_plot=T, + save_rds=T, + plot_probabilities=F, + plot_steps=F, + BayesMaxPNormal=0.5, + leiden_function='modularity' + ) + + }, + error = function(e){ + flog.error(paste(samplename, 'encountered error')) + message('Caught an error!') + print(e) + infercnv_obj=NA + + }, + finally = { + message('All done') + } + ) + + return(infercnv_obj) + +} + +# Run InferCNV on Dara and CART, with bmcite as normal reference +flog.threshold(ERROR) +options(include.full.call.stack = FALSE) +flog.appender(appender.file("replicate_bmcite.log")) +folder = paste0(manifest$meta$work, '/infercnv/replicate_bmcite') +dir.create(folder, recursive=T) +get_infercnv(seuratobj_merged, pval=0.5, subcluster='leiden', folder=folder) + +#Looking at infercnv.png in the output folder, the main differences in CNV clusters are in chromosome 13 and 19. +#The dendrogram didn't separate CNV clusters by these regions well because clustering was performed across all genes, which is too noisy. +#Here we redo clustering based on genes in chromosomes 13 and 19 only. +patient1_cnvobj=readRDS(paste0(manifest$meta$work, '/infercnv/replicate_bmcite/patient1_bmcite_batch1/run.final.infercnv_obj')) +observation_index=patient1_cnvobj@observation_grouped_cell_indices[[1]] +vicinity_genes=rownames(patient1_cnvobj@gene_order[patient1_cnvobj@gene_order$chr %in% c(13,19), ]) +patient_1_distance=parallelDist(t(patient1_cnvobj@expr.data[vicinity_genes,observation_index]), threads=20) + +hc <- hclust(patient_1_distance, method='ward.D2') + +#Plot the results of clustering on chromosomes 13 and 19. +patient1_cnvobj@tumor_subclusters$hc$malignant_plasmacells=NULL +patient1_cnvobj@tumor_subclusters$hc$all_observations=hc +plotoutput = plot_cnv( + patient1_cnvobj, k_obs_groups=3, cluster_by_groups = F, title='patient1_chr1319', + out_dir = paste0(manifest$meta$work, '/infercnv/replicate_bmcite/'), + output_filename='patient1_chr1319', output_format = 'png' +) + +# Split patient1 into the 3 CNV clusters for performing InferCNV +splitted=cutree(hc, k=3) +normcells=colnames(seuratobj_merged)[grepl('^normcell', colnames(seuratobj_merged))] + +cluster1_cells=c(names(splitted)[splitted==1], normcells) +cluster2_cells=c(names(splitted)[splitted==2], normcells) +cluster3_cells=c(names(splitted)[splitted==3], normcells) + +seuratobj_patient1_clusters=list( + seuratobj_merged[, cluster1_cells], + seuratobj_merged[, cluster2_cells], + seuratobj_merged[, cluster3_cells] +) +seuratobj_patient1_clusters[[1]]$patient='patient1_cluster1' +seuratobj_patient1_clusters[[2]]$patient='patient1_cluster2' +seuratobj_patient1_clusters[[3]]$patient='patient1_cluster3' +rm(patient1_cnvobj, seuratobj_merged) + +# Run InferCNV on the 3 CNV clusters separately +flog.threshold(ERROR) +options(include.full.call.stack = FALSE) +flog.appender(appender.file("replicate_clusterlevel_rerun.log")) +folder = paste0(manifest$meta$work, '/infercnv/patient1_clusterlevel') +dir.create(folder, recursive=T) +infercnv_results=mclapply(seuratobj_patient1_clusters, get_infercnv, pval=0.5, subcluster='leiden', + folder=folder, analysis_mode='samples', mc.cores=detectCores()) + +saveRDS(splitted, paste0(folder, "/splitted.Rds")) + diff --git a/code/figure_scripts/supplement_monocle3.R b/code/figure_scripts/supplement_monocle3.R new file mode 100644 index 0000000..3ef7ab6 --- /dev/null +++ b/code/figure_scripts/supplement_monocle3.R @@ -0,0 +1,327 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", + "stringr", "Seurat", "tibble", "tidyr", "cowplot", "scico", + "ggpubr", "ggsci", "ggrastr", "scales", "viridis" +) +.bioc_packages = c() + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +if (any(!"SeuratWrappers" %in% installed.packages())) { + remotes::install_github('satijalab/seurat-wrappers') +} +library(SeuratWrappers) + +if (any(!"SignatuR" %in% installed.packages())) { + remotes::install_github("carmonalab/SignatuR") +} +library(SignatuR) + +if (any(!"monocle3" %in% installed.packages())) { + Sys.unsetenv("GITHUB_PAT") + devtools::install_github('cole-trapnell-lab/monocle3') +} +library(monocle3) + + +source("code/helper/styles.R") +theme_set(mytheme(base_size = 7)) + +### + +get_earliest_principal_node <- function(cds){ + cell_ids <- which(colData(cds)[, "GROUP"] == "Pre-infusion" & grepl("CD8.CM|NaiveLike" , colData(cds)[, "celltype"])) + # cell_ids <- which(colData(cds)[, "GROUP"] == "Pre-infusion") + closest_vertex = cds@principal_graph_aux[["UMAP"]]$pr_graph_cell_proj_closest_vertex + closest_vertex = as.matrix(closest_vertex[colnames(cds), ]) + root_pr_nodes = igraph::V(principal_graph(cds)[["UMAP"]])$name[as.numeric(names(which.max(table(closest_vertex[cell_ids,]))))] + root_pr_nodes +} + +plot_trajectories=function( + seuratobj, + color_cells_by = "color_cells", + dot.size = 1, + nbr.dims = 20, + cluster.res = 1e-2 +){ + + seuratobj = DietSeurat(seuratobj) + seuratobj@meta.data = droplevels(seuratobj@meta.data) + + bl <- c( + SignatuR::GetSignature(SignatuR$Hs$Compartments$Mito), + SignatuR::GetSignature(SignatuR$Hs$Compartments$TCR), + SignatuR::GetSignature(SignatuR$Hs$Compartments$Immunoglobulins) + ) + bl <- unique(bl) + + seurat.l = SplitObject(seuratobj, split.by = "orig.ident") + seurat.l[[1]] = seurat.l[[1]][!rownames(seurat.l[[1]]) %in% bl, ] + seurat.l[[2]] = seurat.l[[2]][!rownames(seurat.l[[2]]) %in% bl, ] + seurat.l[[1]] = FindVariableFeatures(seurat.l[[1]]) + seurat.l[[2]] = FindVariableFeatures(seurat.l[[2]]) + features = SelectIntegrationFeatures(object.list = seurat.l, nfeatures = 1000) + VariableFeatures(seuratobj) = features + seuratobj = seuratobj %>% + ScaleData() %>% + RunPCA() %>% RunUMAP(reduction = "pca", dims = 1:nbr.dims) + + cds<- as.cell_data_set(seuratobj) + cds <- cluster_cells(cds, resolution = cluster.res) + cds <- learn_graph(cds, use_partition = F) + cds <- order_cells(cds, root_pr_nodes=get_earliest_principal_node(cds)) + + plot(plot_cells(cds, color_cells_by = "cluster", cell_size = 1, label_leaves=FALSE, graph_label_size = 2.5)) + + p2 = + plot_cells( + cds, + color_cells_by = "pseudotime", + cell_size = dot.size, + cell_stroke = dot.size, + label_groups_by_cluster=T, + label_leaves=FALSE, + label_branch_points=FALSE, + label_cell_groups = F, + graph_label_size = 2.5 + ) + + ggtitle("\n") + + mytheme(base_size = 7) + + theme( + legend.position = "bottom", + legend.justification = c(.5, 1), + legend.title.align = 1 + ) + + guides(color = guide_colorbar( + title.vjust = 1, barwidth = unit(4, 'lines'), barheight = unit(.4, 'lines'), + ticks.linewidth = 1.5/.pt + )) + + viridis::scale_color_viridis(option='plasma', breaks = scales::pretty_breaks(n = 3)) + + labs(colour = "pseudotime") + + p1 = + plot_cells( + cds, + color_cells_by = color_cells_by, + cell_size = dot.size, + cell_stroke = dot.size, + label_groups_by_cluster = F, + label_leaves = FALSE, + label_branch_points = FALSE, + label_cell_groups = F, + graph_label_size = 2.5 + ) + + ggtitle("\n") + + mytheme(base_size = 7) + + theme( + legend.title = element_blank(), + legend.position = "bottom", + legend.justification = c(.5,1) + ) + + scale_color_manual(values = ggsci::pal_jco(palette = c("default"), alpha = 1)(n=10)[c(2,5,9)]) + + guides(color = guide_legend(ncol = 1, override.aes = list(shape = 16, size = 3))) + + p3 = + plot_cells( + cds, + color_cells_by = "celltype", + cell_size = dot.size,, + cell_stroke = dot.size,, + label_groups_by_cluster = F, + label_leaves = FALSE, + label_branch_points = FALSE, + label_cell_groups = F, + graph_label_size = 2.5 + ) + + ggtitle("\n") + + mytheme(base_size = 7) + + theme( + legend.title = element_blank(), + legend.position = "bottom", + legend.justification = c(.5,1) + ) + + guides(color = guide_legend(ncol = 2, override.aes = list(shape = 16, size = 3))) + + scale_color_manual(values = til.col) + + + cds_degs <- graph_test(cds, neighbor_graph = "principal_graph", cores = 20) + rowData(cds)$gene_short_name = rownames(cds) + + pseudotime_genes_toplot = cds_degs %>% filter(q_value<0.05) %>% + rownames_to_column('genes') %>% + arrange( -morans_test_statistic) %>% + filter( !grepl('^RP[LS]', genes),!grepl('^IG[KLH]V|^TR(B|A)V', genes)) %>% + slice_head(n=9) %>% select(genes) %>% unlist + + p4 = + plot_cells( + cds, + genes = pseudotime_genes_toplot, + cell_size = dot.size, + cell_stroke = dot.size, + show_trajectory_graph=FALSE, + label_cell_groups=FALSE, + label_leaves=FALSE, + scale_to_range = T + ) + + mytheme(base_size = 7) + + theme( + strip.text.x = element_text(size = 7), + panel.spacing = unit(1, "lines"), + legend.position = "bottom", + legend.title.align = 1 + ) + + guides(color = guide_colorbar( + title.vjust = 1, barwidth = unit(4, 'lines'), barheight = unit(.4, 'lines'), + ticks.linewidth = 1.5/.pt + )) + + viridis::scale_color_viridis(option='viridis', breaks = scales::pretty_breaks(n = 3)) + + labs(colour = "% Max") + + return(list(a = p1, b = p2, c = p3, d = p4, cds_degs = cds_degs, cds = cds)) +} + +save_pl = function(obj = NULL, filename = NULL, title = NULL) { + + plot = plot_grid( + plot_grid( + obj$a, + NULL, + obj$c, + NULL, + obj$b, + nrow = 1, align = "vh", rel_widths = c(1, .05, 1, .05, 1), + labels = c("a", "", "b", "", "c"), label_fontface = "bold", label_size = 9 + ), + NULL, + plot_grid( + obj$d, + labels = c("d"), label_fontface = "bold", label_size = 9 + ), + nrow = 3, rel_heights = c(1, .05, 1.95) + ) + + title <- ggdraw() + + draw_label(title, fontface = 'plain', size = 9) + + ggsave2( + filename = filename, + plot_grid(title , plot, nrow = 2, rel_heights = c(.025, 1)), + width = 180, height = 230, units = "mm", scale = 1, bg = "white", dpi = 200 + ) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.t = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_grieb_clonotypes.Rds")) + +se.t@meta.data = se.t@meta.data %>% mutate( + color_cells = case_when( + GROUP == "Pre-infusion" ~ "Apheresis", + CAR_BY_EXPRS == T & GROUP == "Post-infusion" ~ "Post CAR+", + CAR_BY_EXPRS == F & GROUP == "Post-infusion" ~ "Post CAR-", + TRUE ~ "something wrong" + ) +) +se.t = se.t[, se.t$CellCycle == F] + +p12_CD4 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD4 T-Cell" & TISSUE_SOURCE == "PBMC" & PATIENT_ID == "Patient 012"), + dot.size = .5 +) +save_pl( + obj = p12_CD4, filename = "code/figures/supplement/monocle_p12_cd4.png", + title = "Single CD4+ T cell trajectories before/after Ide-cel (P12)" +) + +p12_CD8 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD8 T-Cell" & TISSUE_SOURCE == "PBMC" & PATIENT_ID == "Patient 012"), + dot.size = .25, cluster.res = 0.002 +) +save_pl( + p12_CD8, "code/figures/supplement/monocle_p12_cd8.png", + "Single CD8+ T cell trajectories before/after Ide-cel (P12)" +) + +p14_CD4 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD4 T-Cell" & TISSUE_SOURCE == "PBMC" & PATIENT_ID == "Patient 014"), + dot.size = .5 +) +save_pl( + p14_CD4, "code/figures/supplement/monocle_p14_cd4.png", + "Single CD4+ T cell trajectories before/after Ide-cel (P14)" +) + +p14_CD8 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD8 T-Cell" & TISSUE_SOURCE == "PBMC" & PATIENT_ID == "Patient 014"), + dot.size = .5 +) +save_pl( + p14_CD8, "code/figures/supplement/monocle_p14_cd8.png", + "Single CD8+ T cell trajectories before/after Ide-cel (P14)" +) + +p7_CD4 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD4 T-Cell" & PATIENT_ID == "Patient 007"), + dot.size = .5 +) +save_pl( + p7_CD4, "code/figures/supplement/monocle_p7_cd4.png", + "Single CD4+ T cell trajectories before/after Ide-cel (P07)" +) + +p7_CD8 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD8 T-Cell" & PATIENT_ID == "Patient 007"), + dot.size = .5, cluster.res = 0.02 +) +save_pl( + p7_CD8, "code/figures/supplement/monocle_p7_cd8.png", + "Single CD8+ T cell trajectories before/after Ide-cel (P07)" +) + +p8_CD4 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD4 T-Cell" & PATIENT_ID == "Patient 008"), + dot.size = .5 +) +save_pl( + p8_CD4, "code/figures/supplement/monocle_p8_cd4.png", + "Single CD4+ T cell trajectories before/after Ide-cel (P08)" +) + +p8_CD8 = plot_trajectories( + seuratobj = subset(se.t, subset = celltype_short_2 == "CD8 T-Cell" & PATIENT_ID == "Patient 008"), + dot.size = .5 +) +save_pl( + p8_CD8, "code/figures/supplement/monocle_p8_cd8.png", + "Single CD8+ T cell trajectories before/after Ide-cel (P08)" +) + + diff --git a/code/figure_scripts/supplement_pbmc_post_vs_pre.R b/code/figure_scripts/supplement_pbmc_post_vs_pre.R new file mode 100644 index 0000000..aff75eb --- /dev/null +++ b/code/figure_scripts/supplement_pbmc_post_vs_pre.R @@ -0,0 +1,253 @@ +.cran_packages = c( + "yaml", "ggplot2","reshape2", "dplyr", "naturalsort", "devtools", "scales", + "stringr", "Seurat", "tibble", "tidyr", "forcats", "scCustomize", "openxlsx", + "rlang", "patchwork", "cowplot", "ggrepel", "scico", "parallel", "circlize" +) +.bioc_packages = c("org.Hs.eg.db", "clusterProfiler", "BiocParallel") + +if (any(!"enrichplot" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("YuLab-SMU/enrichplot") +} +library(enrichplot) + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + + +if (any(!"presto" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("immunogenomics/presto") +} +library(presto) + +if (any(!"speckle" %in% installed.packages())) { + remotes::install_github("phipsonlab/speckle", build_vignettes = TRUE, dependencies = "Suggest") +} +# browseVignettes("speckle") +library(speckle) + +source("code/helper/styles.R") +source("code/helper/functions.R") +source("code/helper/functions_plots.R") +source("code/helper/italk_utilities.R") +theme_set(mytheme(base_size = 8)) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# LOAD DATA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "integration/seurat_harmony_pub.Rds")) +se.meta = subset(se.meta, TISSUE_SOURCE == "PBMC") + +se.cr = subset(se.meta, subset = RESPONSE_CONSENSUS == "CR") +se.cr@meta.data = droplevels(se.cr@meta.data) +se.noncr = subset(se.meta, subset = RESPONSE_CONSENSUS == "nonCR") +se.noncr@meta.data = droplevels(se.noncr@meta.data) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Celltype composition per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +p.data = se.meta@meta.data +t = "celltype_short_2" + +comp.pl = sc_ct_sample_fraction( + inpMeta = p.data, + label = t, group.facet = "RESPONSE_CONSENSUS", + group.color = "GROUP", dot.color = "PRODUCT", + nbr.cell.cut = 50, group.psz = .75, scales = "free_x" +) + + scale_y_continuous( + trans = scales::pseudo_log_trans(sigma = 0.0001, base = 10), + breaks=c(0, 0.001, 0.01, 0.1, 1) + ) + + coord_cartesian(ylim=c(0, 2)) + +# Speckle +df = droplevels(subset(p.data, RESPONSE_CONSENSUS == "CR")) +res.1 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$GROUP, transform = "asin" +) %>% mutate(Group = "CR") + +df = droplevels(subset(p.data, RESPONSE_CONSENSUS == "nonCR")) +res.2 = speckle::propeller( + clusters = df[[t]], sample = df$orig.ident, group = df$GROUP, transform = "asin" +) %>% mutate(Group = "nonCR") + +res.comb = rbind(res.1, res.2) +res.comb$ID = paste0(res.comb$BaselineProp.clusters, "_", res.comb$Group) + +pl.dat = dplyr::distinct(comp.pl$data, celltype, group_facet) +pl.dat$ID = paste0(pl.dat[,1], "_", pl.dat[,2]) + +pl.dat$pval = res.comb$P.Value[match(pl.dat$ID, res.comb$ID)] +pl.dat$yloc = max(comp.pl$data$perc_sample) + .75 +pl.dat = add_signif(pl.dat, "pval", "pval_star", pval.relax = T) + +comp.pl = + comp.pl + geom_text(data = pl.dat, aes(y = yloc, label = pval_star), size = 4, position = position_dodge(width = .75)) + + guides(fill = guide_legend(title = NULL, order = 1)) + + guides(color = guide_legend(title = NULL, override.aes = list(shape = 16, size = 3.5))) + + scale_color_manual(values = c("#994455", "black")) + + theme( + plot.title = element_text(size = 10, hjust = 0.5, face = "plain"), + legend.margin = margin(t=-5) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA: Post vs. Pre +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# CR +obj = se.cr +obj = subset(obj, CAR_BY_EXPRS == F) + +res.cr = run_wilx( + obj = obj, target = "celltype_short_2", min.cells = 50, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +res.cr.sign = subset(res.cr, significant == T) +ct.keep = names(table(res.cr.sign$cluster)[table(res.cr.sign$cluster) > 10]) +res.cr.sign = res.cr.sign[res.cr.sign$cluster %in% ct.keep, ] +table(res.cr.sign$cluster) + +dgea.cr.pl = dgea_plot( + dgea.res = res.cr, dgea.res.sign = res.cr.sign, + ylim.extend.up = 1, ylim.extend.dn = 1 +) +dgea.cr.pl = dgea.cr.pl + + ggtitle("DEG in CR comparing post- with pre-infusional PBMC\n") + + theme(plot.title = element_text(size = 8, hjust = 0.5, face = "plain")) + +# nonCR +obj = se.noncr +obj = subset(obj, CAR_BY_EXPRS == F) + +res.noncr = run_wilx( + obj = obj, target = "celltype_short_2", min.cells = 50, + contrast.group = "GROUP", contrast = c("Post-infusion", "Pre-infusion") +) + +res.noncr.sign = subset(res.noncr, significant == T) +ct.keep = names(table(res.noncr.sign$cluster)[table(res.noncr.sign$cluster) > 10]) +res.noncr.sign = res.noncr.sign[res.noncr.sign$cluster %in% ct.keep, ] +table(res.noncr.sign$cluster) + +dgea.noncr.pl = dgea_plot( + dgea.res = res.noncr, dgea.res.sign = res.noncr.sign, + ylim.extend.up = 1.5, ylim.extend.dn = 1 +) +dgea.noncr.pl = dgea.noncr.pl + + ggtitle("DEG in nonCR comparing post- with pre-infusional PBMC\n") + + theme(plot.title = element_text(size = 8, hjust = 0.5, face = "plain")) + + +# write to xlxs +wb = loadWorkbook("code/tables/Supplementaltable3.xlsx") +write_to_xlsx(df = res.cr.sign, filename = "code/tables/Supplementaltable3.xlsx", sheet = "CR; post vs pre", wb = wb) +write_to_xlsx(df = res.noncr.sign, filename = "code/tables/Supplementaltable3.xlsx", sheet = "nonCR; post vs pre", wb = wb) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ORA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +df.ora = rbind( + res.cr.sign %>% mutate(SOURCE = "CR"), + res.noncr.sign %>% mutate(SOURCE = "nonCR") +) +eg = bitr(res.cr$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +df.ora$ENTREZID = eg$ENTREZID[match(df.ora$feature, eg$SYMBOL)] +df.ora = df.ora[!is.na(df.ora$ENTREZID), ] + +df.ora$ID = paste0(df.ora$cluster, ".", df.ora$SOURCE) +geneList = lapply(split(df.ora, df.ora$ID), function(x){ + setNames(x$ENTREZID, x$logFC) +}) + +universe = bitr(res.cr$feature, fromType="SYMBOL", toType="ENTREZID", OrgDb="org.Hs.eg.db") +universe = eg[!is.na(eg$ENTREZID), ]$ENTREZID + +ora.cc <- compareCluster( + ENTREZID~cluster+SOURCE, + data = df.ora, + OrgDb = org.Hs.eg.db, + fun = "enrichGO", + ont = "BP", + pAdjustMethod = "BH", + pvalueCutoff = 0.05, + universe = unique(universe) +) +ora.cc = simplify(ora.cc) + +ora.all.pl = + ora_dotplot_cc( + ora.res = ora.cc, + genelist = geneList, + order.by.p = F, + nbr.tops = 7, + min.size = 5, + term.length = 100, + gg.title = "", + base.size = 8, + quantile.cut = T, + x.text.size = rel(1), + facet = T, + facet.order = c(3, 2) + ) + + ggtitle(NULL) + + theme( + axis.text.y = element_text(size = 8, lineheight = .75) + ) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# plot +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +set.seed(2222) +ggsave2( + filename="code/figures/supplement/PBMC_Post_vs_Pre.pdf", + plot_grid( + plot_grid( + plot_grid(NULL, comp.pl, ncol = 1, rel_heights = c(.025, 1)), + NULL, + plot_grid(NULL, dgea.cr.pl, NULL, dgea.noncr.pl, nrow = 4, rel_heights = c(0.025, 1, .1, 1)), + ncol = 3, rel_widths = c(1, .1, 1.5), + labels = c("a", "", "b"), label_fontface = "bold", label_size = 12 + ), + NULL, + plot_grid( + ora.all.pl + ), + nrow = 3, rel_heights = c(1.6, .1, 2), + labels = c("", "", "c"), label_fontface = "bold", label_size = 12 + ), + width = 210, + height = 250, + dpi = 300, + # bg = NULL, + bg = "white", + units = "mm", + scale = 1.6 +) + diff --git a/code/figure_scripts/supplemental_table_1.R b/code/figure_scripts/supplemental_table_1.R new file mode 100644 index 0000000..deb7f66 --- /dev/null +++ b/code/figure_scripts/supplemental_table_1.R @@ -0,0 +1,263 @@ +.cran_packages = c( + "yaml", "reshape2", "dplyr", "tidyverse", "openxlsx", "stringr" +) +.bioc_packages = c() + +# Install CRAN packages (if not already installed) +.inst = .cran_packages %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +# Install bioconductor packages (if not already installed) +.inst <- .bioc_packages %in% installed.packages() +if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) +} + +list.of.packages = c(.cran_packages, .bioc_packages) + +## Loading library +for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) +} + +Read_Metrics_10X_Multi <- function( + base_path, + secondary_path = "outs/per_sample_outs/", + lib_list = NULL, + col_values = "Metric.Value" +) { + + library(cli) + library(dplyr) + + # Confirm directory exists + if (dir.exists(paths = base_path) == FALSE) { + cli_abort(message = "Directory: {.val {base_path}} specified by {.code base_path} does not exist.") + } + # Detect libraries if lib_list is NULL + if (is.null(x = lib_list)) { + lib_list <- list.dirs(path = base_path, full.names = F, recursive = F) + } + + # Check if full directory path exists + for (i in 1:length(x = lib_list)) { + full_directory_path <- file.path(base_path, lib_list[i], secondary_path) + if (dir.exists(paths = full_directory_path) == FALSE) { + cli_abort(message = "Full Directory does not exist {.val {full_directory_path}} was not found.") + } + } + + raw_data_list <- lapply(1:length(x = lib_list), function(x) { + file_path <- file.path(base_path, lib_list[x], paste0(secondary_path, "/", lib_list[x], "/")) + raw_data <- read.csv(file = paste0(file_path, "metrics_summary.csv"), stringsAsFactors = F) + return(raw_data) + }) + names(raw_data_list) <- lib_list + full_data <- dplyr::bind_rows(raw_data_list, .id = "sample_id") + + # make value numeric + full_data$quantity = ifelse(grepl("%", full_data$Metric.Value), "PERC", "COUNT" ) + row_numbers <- grep(pattern = ",", x = full_data[, col_values]) + full_data[row_numbers, ][, col_values] = as.numeric(gsub(",", "", full_data[row_numbers, ][, col_values])) + full_data[, col_values] = gsub("%", "", full_data[, col_values]) + full_data[, col_values] = as.numeric(full_data[, col_values]) + full_data$Metric.Name = paste0(full_data$Metric.Name, "_", full_data$quantity) + + raw.gex = full_data[full_data$Library.Type == "Gene Expression", ] + raw.gex.cells = raw.gex[raw.gex$Category == "Cells", ] + raw.gex.seq = raw.gex[raw.gex$Grouped.By == "Fastq ID", ] + raw.gex.mapping = raw.gex[grepl("Confidently", raw.gex$Metric.Name), ] + raw.gex.mapping = subset(raw.gex.mapping, Category != "Cells") + raw.gex.physical = raw.gex[!rownames(raw.gex) %in% rownames(rbind(raw.gex.cells, raw.gex.seq, raw.gex.mapping)), ] + raw.gex.physical = subset(raw.gex.physical, Metric.Name != "Estimated number of cells_COUNT") # Redundant + + raw.adt = full_data[full_data$Library.Type == "Antibody Capture", ] + raw.adt.cells = raw.adt[raw.adt$Category == "Cells", ] + raw.adt.seq = raw.adt[raw.adt$Grouped.By == "Fastq ID", ] + raw.adt.physical = raw.adt[!rownames(raw.adt) %in% rownames(rbind(raw.adt.cells, raw.adt.seq)), ] + raw.adt.physical = subset(raw.adt.physical, Metric.Name != "Estimated number of cells_COUNT") # Redundant + + raw.vdj.t = full_data[full_data$Library.Type == "VDJ T", ] + raw.vdj.t.cells = raw.vdj.t[raw.vdj.t$Category == "Cells", ] + raw.vdj.t.cells.vdj.anno = raw.vdj.t.cells[grepl("^Cells|Paired clonotype", raw.vdj.t.cells$Metric.Name), ] + raw.vdj.t.cells.texprs = raw.vdj.t.cells[!rownames(raw.vdj.t.cells) %in% rownames(raw.vdj.t.cells.vdj.anno), ] + raw.vdj.t.seq = raw.vdj.t[raw.vdj.t$Grouped.By == "Fastq ID", ] + raw.vdj.t.ph = raw.vdj.t[!rownames(raw.vdj.t) %in% rownames(rbind(raw.vdj.t.cells, raw.vdj.t.seq)), ] + raw.vdj.t.ph = subset(raw.vdj.t.ph, Metric.Name != "Estimated number of cells_COUNT") # Redundant + raw.vdj.t.mapping = raw.vdj.t.ph[grepl("Reads mapped", raw.vdj.t.ph$Metric.Name), ] + raw.vdj.t.physical = raw.vdj.t.ph[!grepl("Reads mapped", raw.vdj.t.ph$Metric.Name), ] + + raw.vdj.b = full_data[full_data$Library.Type == "VDJ B", ] + raw.vdj.b.cells = raw.vdj.b[raw.vdj.b$Category == "Cells", ] + raw.vdj.b.cells.vdj.anno = raw.vdj.b.cells[grepl("^Cells|Paired clonotype", raw.vdj.b.cells$Metric.Name), ] + raw.vdj.b.cells.bexprs = raw.vdj.b.cells[!rownames(raw.vdj.b.cells) %in% rownames(raw.vdj.b.cells.vdj.anno), ] + raw.vdj.b.seq = raw.vdj.b[raw.vdj.b$Grouped.By == "Fastq ID", ] + raw.vdj.b.ph = raw.vdj.b[!rownames(raw.vdj.b) %in% rownames(rbind(raw.vdj.b.cells, raw.vdj.b.seq)), ] + raw.vdj.b.ph = subset(raw.vdj.b.ph, Metric.Name != "Estimated number of cells_COUNT") # Redundant + raw.vdj.b.mapping = raw.vdj.b.ph[grepl("Reads mapped", raw.vdj.b.ph$Metric.Name), ] + raw.vdj.b.physical = raw.vdj.b.ph[!grepl("Reads mapped", raw.vdj.b.ph$Metric.Name), ] + + res = list( + "GEX" = list( + "Cell Metrics" = raw.gex.cells[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Sequencing Metrics" = raw.gex.seq[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Mapping Metrics" = raw.gex.mapping[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Metrics Per Physical Library" = raw.gex.physical[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value) + ), + "ADT" = list( + "Cell Metrics" = raw.adt.cells[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Sequencing Metrics" = raw.adt.seq[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Metrics Per Physical Library" = raw.adt.physical[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value) + ), + "VDJ_T" = list( + "T Cell Expression" = raw.vdj.t.cells.texprs[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "V(D)J Annotation" = raw.vdj.t.cells.vdj.anno[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Sequencing Metrics" = raw.vdj.t.seq[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Mapping Metrics" = raw.vdj.t.mapping[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Metrics Per Physical Library" = raw.vdj.t.physical[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value) + ), + "VDJ_B" = list( + "B Cell Expression" = raw.vdj.b.cells.bexprs[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "V(D)J Annotation" = raw.vdj.b.cells.vdj.anno[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Sequencing Metrics" = raw.vdj.b.seq[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Mapping Metrics" = raw.vdj.b.mapping[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value), + "Metrics Per Physical Library" = raw.vdj.b.physical[, c("sample_id", "Metric.Name", "Metric.Value")] %>% tidyr::spread(Metric.Name, Metric.Value) + ) + ) + + return(res) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +manifest = yaml.load_file("manifest.yaml") + +se.meta = readRDS(paste0(manifest$meta$work, "seurat_pre_anno_pub.Rds")) +pdata = read.csv2(manifest$grieb$phenodata, sep = ",", na.strings="-", check.names = T) +metrics = Read_Metrics_10X_Multi(base_path = paste0(manifest$grieb$data_dl, "/cellranger")) +sample.quality = read.xlsx("data/sample.quality.xlsx", check.names = F) + +metrics.gex = lapply(metrics$GEX, function(x){ + x$sample_id = gsub("multi_", "", x$sample_id) + colnames(x) = gsub("_PERC", " (%)", gsub("_COUNT", "", colnames(x))) + x +}) + +gex.a = metrics.gex$`Metrics Per Physical Library` %>% + select("Valid barcodes (%)", "Valid UMIs (%)") +gex.b = metrics.gex$`Mapping Metrics` %>% + select("Confidently mapped to transcriptome (%)") +gex.c = metrics.gex$`Cell Metrics` %>% + select(-"Total genes detected") %>% + relocate(Cells, .after = last_col()) %>% + rename("No. of valid cell barcodes before QC filtering" = Cells) +gex = cbind(gex.a, gex.b, gex.c) +rownames(gex) = gex$sample_id +gex$sample_id = NULL + +nbr.cells.post.qc = data.frame(table(se.meta$orig.ident)) +rownames(nbr.cells.post.qc) = nbr.cells.post.qc$Var1 +nbr.cells.post.qc = nbr.cells.post.qc[rownames(gex), ] +gex = cbind(gex, "No. of valid cell barcodes after QC filtering" = nbr.cells.post.qc$Freq) + +gex$"% Passed cells (after QC/before QC)" = gex$`No. of valid cell barcodes after QC filtering` / + gex$`No. of valid cell barcodes before QC filtering` * 100 + +metrics.adt = lapply(metrics$ADT, function(x){ + x$sample_id = gsub("multi_", "", x$sample_id) + colnames(x) = gsub("_PERC", " (%)", gsub("_COUNT", "", colnames(x))) + x +}) +adt.a = metrics.adt$`Metrics Per Physical Library` %>% + select( + "Valid barcodes (%)", "Valid UMIs (%)", "Fraction antibody reads (%)", + "Fraction antibody reads usable (%)", "Mean reads per cell" + ) +adt.b = metrics.adt$`Cell Metrics` %>% + select(-"Cells") +adt = cbind(adt.a, adt.b) +rownames(adt) = adt$sample_id +adt$sample_id = NULL + +metrics.vdj.t = lapply(metrics$VDJ_T, function(x){ + x$sample_id = gsub("multi_", "", x$sample_id) + colnames(x) = gsub("_PERC", " (%)", gsub("_COUNT", "", colnames(x))) + x +}) +tcell.a = metrics.vdj.t$`T Cell Expression` %>% + select("sample_id", "Estimated number of cells") %>% + rename("No. of cells annotated as T cells" = "Estimated number of cells") +tcell.b = metrics.vdj.t$`Metrics Per Physical Library` %>% + select("Valid barcodes (%)", "Mean used reads per cell") +tcell.c = metrics.vdj.t$`V(D)J Annotation` %>% + select("Cells with productive V-J spanning pair (%)") +tcell.d = metrics.vdj.t$`Mapping Metrics` %>% + select("Reads mapped to any V(D)J gene (%)") +tcell = cbind(tcell.a, tcell.b, tcell.c, tcell.d) +rownames(tcell) = tcell$sample_id +tcell$sample_id = NULL + +metrics.vdj.b = lapply(metrics$VDJ_B, function(x){ + x$sample_id = gsub("multi_", "", x$sample_id) + colnames(x) = gsub("_PERC", " (%)", gsub("_COUNT", "", colnames(x))) + x +}) +bcell.a = metrics.vdj.t$`T Cell Expression` %>% + select("sample_id", "Estimated number of cells") %>% + rename("No. of cells annotated as B/Plasma cells" = "Estimated number of cells") +bcell.b = metrics.vdj.t$`Metrics Per Physical Library` %>% + select("Valid barcodes (%)", "Mean used reads per cell") +bcell.c = metrics.vdj.t$`V(D)J Annotation` %>% + select("Cells with productive V-J spanning pair (%)") +bcell.d = metrics.vdj.t$`Mapping Metrics` %>% + select("Reads mapped to any V(D)J gene (%)") +bcell = cbind(bcell.a, bcell.b, bcell.c, bcell.d) +rownames(bcell) = bcell$sample_id +bcell$sample_id = NULL + +seq.metrics = cbind(gex, adt, tcell, bcell) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +sample.quality$Sample.ID = stringr::str_trim(sample.quality$Sample.ID) +pdata = merge(pdata, sample.quality, by.x = "barcode", by.y = "Sample.ID") + +pdata$name = gsub("Patient 0", "P", pdata$name) +pdata = pdata %>% + select( + "Sample ID" = SAMPLE_NAME, "Patient" = name, "Days since apheresis" = days.from.apharesis, + "Days since CAR infusion" = days.from.infusion, "Source" = source, + "Remission after CAR" = remission.after.CAR, "Cells.after.thawing.(x106)", + "Viability.after.thawing.(%)", "Viability.after.dead.cell.removal.(%)" + ) + +seq.metrics = seq.metrics[pdata$`Sample ID`, ] +stopifnot(identical(pdata$`Sample ID`, rownames(seq.metrics))) + +df.fin = cbind(pdata, seq.metrics) +df.fin = df.fin[order(df.fin$Patient), ] +sheet = "table" +filename = "code/tables/Supplementaltable1.xlsx" +wb <- createWorkbook() +addWorksheet(wb, sheet) +writeData( + wb, sheet, + df.fin, + startRow = 1, startCol = 1) +saveWorkbook(wb, filename, overwrite = T) + + + + + + diff --git a/code/figures/main/README b/code/figures/main/README new file mode 100644 index 0000000..e69de29 diff --git a/code/figures/supplement/README b/code/figures/supplement/README new file mode 100644 index 0000000..e69de29 diff --git a/code/helper/adt_rna_gene_mapping.R b/code/helper/adt_rna_gene_mapping.R new file mode 100644 index 0000000..1dac5db --- /dev/null +++ b/code/helper/adt_rna_gene_mapping.R @@ -0,0 +1,84 @@ +adt.rna.mapping = c( + "CCR10" = "CCR10", + "CD103" = "ITGAE", + "CD107A" = "LAMP1", + "CD11B" = "ITGAM", + "CD11C" = "ITGAX", + "CD122" = "IL2RB", + "CD123" = "IL3RA", + "CD124" = "IL4R", + "CD127" = "IL7R", + "CD13" = "ANPEP", + "CD14" = "CD14", + "CD134" = "TNFRSF4", + "CD137" = "TNFRSF9", + "CD138" = "SDC1", + "CD141" = "THBD", + "CD152" = "CTLA4", + "CD154" = "CD40LG", + "CD16" = "FCGR3A", + "CD161" = "KLRB1", + "CD117" = "KIT", + "CD183" = "CXCR3", + "CD185" = "CXCR5", + "CD19" = "CD19", + "CD194" = "CCR4", + "CD196" = "CCR6", + "CD197" = "CCR7", + "CD20" = "MS4A1", + "CD21" = "CR2", + "CD23" = "FCER2", + "CD223" = "LAG3", + "CD244" = "CD244", + "CD25" = "IL2RA", + "CD26" = "DPP4", + "CD28" = "CD28", + "CD268" = "TNFRSF13C", + "CD272" = "BTLA", + "CD274" = "CD274", + "CD278" = "ICOS", + "CD279" = "PD1", + "CD29" = "ITGB1", + "CD294" = "PTGDR2", + "CD3" = "CD3G", + "CD303" = "CLEC4C", + "CD304" = "NRP1", + "CD314" = "KLRK1", + "CD319" = "SLAMF7", + "CD33" = "CD33", + "CD335" = "NCR1", + "CD35" = "CR1", + "CD352" = "SLAMF6", + "CD38" = "CD38", + "CD39" = "ENTPD1", + "CD4" = "CD4", + "CD45" = "PTPRC", + "CD45RA" = "PTPRC", # CD45RA and CD45RA are Isoform of PTPRC. in scRNA-seq, we only have PTPRC + "CD45RO" = "PTPRC", # CD45RO and CD45RA are Isoform of PTPRC. in scRNA-seq, we only have PTPRC + "CD49A" = "ITGA1", + "CD49F" = "ITGA6", + "CD54" = "ICAM1", + "CD56" = "NCAM1", + "CD57" = "B3GAT1", + "CD62L" = "SELL", + "CD69" = "CD69", + "CD71" = "TFRC", + "CD73" = "NT5E", + "CD79B" = "CD79B", + "CD8" = "CD8A", + "CD8A" = "CD8A", + "CD85J" = "LILRB1", + "CD88" = "C5AR1", + "CD94" = "KLRD1", + "CD95" = "FAS", + "HLA-DR" = "HLA-DRA", + "IgD" = "IGHD", + "IgM" = "IGHM", + "KLRG1" = "KLRG1", + "TIGIT" = "TIGIT", + "TCR" = "TRAC", + "CD366" = "CD366", + "CD337" = "NCR3", + "CD269" = "TNFRSF17", + "TCRAB" = "TCRAB" +) diff --git a/code/helper/dgea_helper.R b/code/helper/dgea_helper.R new file mode 100644 index 0000000..a38aa41 --- /dev/null +++ b/code/helper/dgea_helper.R @@ -0,0 +1,665 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# make pseudobulk (aggregate rawcounts) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +make_pseudobulk = function( + seurat.obj = se.meta.fltrd, + pb.group = NULL +){ + seurat.obj@meta.data$orig.ident = factor(gsub("\\_", "-", seurat.obj@meta.data$orig.ident)) + seurat.obj@meta.data[[paste0("ORI_NAME_", pb.group)]] = gsub("_", ".", seurat.obj@meta.data[[pb.group]]) + seurat.obj@meta.data[[pb.group]] = gsub("_", ".", seurat.obj@meta.data[[pb.group]]) + + se.pseudo = PseudobulkExpression_custom( + object = seurat.obj, + pb.method = 'aggregate', + assays = "RNA", + return.seurat = T, + group.by = c(pb.group, "orig.ident"), + slot = "counts" + ) + + if(is.null(pb.group)) { + nbr.cells = reshape2::melt(as.matrix(table(seurat.obj$orig.ident))) + nbr.cells = as.data.frame(nbr.cells) + nbr.cells$ID = nbr.cells$Var1 + } else { + nbr.cells = reshape2::melt(as.matrix(table(seurat.obj$orig.ident, seurat.obj@meta.data[[pb.group]]))) + nbr.cells = as.data.frame(nbr.cells) + nbr.cells$ID = paste0(nbr.cells$Var2, "_", nbr.cells$Var1) + } + se.pseudo@meta.data$nCells = nbr.cells$value[match(rownames(se.pseudo@meta.data), nbr.cells$ID)] + + counts = as.matrix(se.pseudo@assays$RNA@counts) + colnames(counts) = unname(colnames(counts)) + + pheno = se.pseudo@meta.data + colnames(pheno) = c("PB_GROUP", "nCOUNTS", "nFEATURES", "nCELLS") + if(is.null(pb.group)){ + pheno$orig.ident = rownames(pheno) + } else { + pheno$orig.ident = gsub(".+\\_", "", rownames(pheno)) + } + + pheno.2 = seurat.obj@meta.data[!duplicated(seurat.obj@meta.data$orig.ident), ] + rn = rownames(pheno) + pheno = dplyr::left_join(pheno, pheno.2, by = "orig.ident") + rownames(pheno) = rn + # we can't use these columns anymore + pheno = pheno[, !grepl("ProjecTILs|RNA_snn", colnames(pheno))] + pheno$ORI_NAME_PB_GROUP = pheno.2[[paste0("ORI_NAME_", pb.group)]][match(pheno$PB_GROUP, pheno.2[[pb.group]])] + + stopifnot(identical(rownames(pheno), colnames(counts))) + dds = edgeR::DGEList(counts = counts, samples = pheno) + log.cpm = edgeR::cpm(dds, log = T) + + gc.anno = seurat.obj@assays$RNA@meta.features + stopifnot(identical(rownames(gc.anno), rownames(dds))) + + se = SummarizedExperiment::SummarizedExperiment( + assays=list(counts = dds$counts, log.cpm = log.cpm), + rowData = gc.anno, colData = dds$samples + ) + + if(is.null(pb.group)){ + se$PB_GROUP = NULL + } else { + se$PB_GROUP = gsub("\\.", "_", se$PB_GROUP) + se$PB_GROUP = gsub(" ", "_", se$PB_GROUP) + se$PB_GROUP = gsub("\\\n", "_", se$PB_GROUP) + se$PB_GROUP = as.factor(se$PB_GROUP) + } + se +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Pseudobulk: QC +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +qc_filter = function( + se.obj = se, + ncells = 25, + pb.grp.column = "PB_GROUP", + pb.ctrst.column = NULL, + min.samples.per.group = 3, + batch.norm = T, + batch.norm.cov = "STUDY" +) { + + colData(se.obj) = droplevels(colData(se.obj)) + + if(!is.null(pb.ctrst.column)) { + na.vals = is.na(se.obj[[pb.ctrst.column]]) + se.obj = se.obj[, !is.na(se.obj[[pb.ctrst.column]])] + colData(se.obj) = droplevels(colData(se.obj)) + + if(any(names(table(na.vals)) == T)) { + print(paste0( + "Nbr. of samples with NA in column ", pb.ctrst.column, ": ", + unname(table(na.vals)[2]), " (are filtered out)" + )) + } + } + + nbr.ori = ncol(se.obj) + se.obj = se.obj[, se.obj$nCELLS > ncells] + colData(se.obj) = droplevels(colData(se.obj)) + print( + paste0("Pseudobulk samples with more than ", ncells, " cells are kept. ", + ncol(se.obj), "/", nbr.ori, " pseudobulk samples retained")) + + if(!is.null(se.obj[[pb.grp.column]]) & !is.null(pb.ctrst.column)) { + + target = pb.ctrst.column + grp = unique(as.character(se.obj[[pb.grp.column]])) + keep.samples = list() + for (i in grp) { + cat("\n") + print(paste0("Group: ", i)) + se.obj.grp = se.obj[, se.obj[[pb.grp.column]] == i] + + cat("Number of pseudobulk samples per group after samples filtering:") + no.smpls.left = table(se.obj.grp[[target]]) + print(no.smpls.left) + + too.small.grps = no.smpls.left[no.smpls.left < min.samples.per.group] + if(length(too.small.grps) > 0) { + cat( + paste0("The Following groups are removed (< ",min.samples.per.group, + " pseudobulk samples):\n", paste(names(too.small.grps), collapse = ", "), "\n" ) + ) + se.obj.grp = se.obj.grp[ ,!se.obj.grp[[target]] %in% names(too.small.grps) ] + colData(se.obj.grp) = droplevels(colData(se.obj.grp)) + } + no.smpls.left = table(se.obj.grp[[target]]) + + if(sum(no.smpls.left >= min.samples.per.group) < 2) { + cat("Only one group left. No DGEA possible\n") + } else { + keep.samples[[i]] = colnames(se.obj.grp) + } + } + + se.obj = se.obj[, colnames(se.obj) %in% unname(unlist(keep.samples))] + colData(se.obj) = droplevels(colData(se.obj)) + + if(!is.null(batch.norm.cov)) { + print(paste0("removeBatchEffect() with factor ", batch.norm.cov, " was performed")) + + removeBatch.check = try(limma::removeBatchEffect(assays(se.obj)$log.cpm, batch = se.obj[[batch.norm.cov]]), silent = T) + if(inherits(removeBatch.check, "try-error") == F){ + log.cpm.adj = limma::removeBatchEffect(assays(se.obj)$log.cpm, batch = se.obj[[batch.norm.cov]]) + assays(se.obj)$log.cpm.adj = log.cpm.adj + } else { + message("removeBatchEffect throws an Error. No batch correction is done") + assays(se.obj)$log.cpm.adj = assays(se.obj)$log.cpm + } + } + + se.obj + + } else { + if(!is.null(se.obj[[pb.grp.column]]) & is.null(pb.ctrst.column)) { + target = pb.grp.column + } + if(is.null(se.obj[[pb.grp.column]]) & !is.null(pb.ctrst.column)) { + target = pb.ctrst.column + } + + cat("Number of pseudobulk samples per group after samples filtering:") + no.smpls.left = table(se.obj[[target]]) + print(no.smpls.left) + + too.small.grps = no.smpls.left[no.smpls.left < min.samples.per.group] + if(length(too.small.grps) > 0) { + cat( + paste0("The Following groups are removed (< ",min.samples.per.group, + " pseudobulk samples):\n", paste(names(too.small.grps), collapse = ", "), "\n" ) + ) + se.obj = se.obj[ ,!se.obj[[target]] %in% names(too.small.grps) ] + colData(se.obj) = droplevels(colData(se.obj)) + } + no.smpls.left = table(se.obj[[target]]) + if(sum(no.smpls.left >= min.samples.per.group) < 2) { + stop("Only one group left. No DGEA possible") + } + + if(!is.null(batch.norm.cov)) { + print(paste0("removeBatchEffect() with factor ", batch.norm.cov, " was performed")) + + removeBatch.check = try(limma::removeBatchEffect(assays(se.obj)$log.cpm, batch = se.obj[[batch.norm.cov]]), silent = T) + if(inherits(removeBatch.check, "try-error") == F){ + log.cpm.adj = limma::removeBatchEffect(assays(se.obj)$log.cpm, batch = se.obj[[batch.norm.cov]]) + assays(se.obj)$log.cpm.adj = log.cpm.adj + } else { + message("removeBatchEffect throws an Error. No batch correction is done") + assays(se.obj)$log.cpm.adj = log.cpm.adj + } + + } + se.obj + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA: Pseudobulk Limma +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pb_limma <- function( + dge_formula, + obj = se.fltrd, + verbose = TRUE, + idents = "PB_GROUP", + vals_test = NULL, + mode = c("one_vs_all", "within")[1], + pval = 0.05 +) { + + all_vars <- unlist( + strsplit(tail(as.character(dge_formula), 1), split = " \\+ ") + ) + if (verbose) { + message(sprintf("All vars: %s", paste(all_vars, collapse = ", "))) + } + contrast_var <- tail(all_vars, 1) + if (verbose) { + message(sprintf("Contrast var: %s", contrast_var)) + } + + if(mode == "within"){ + vals_test <- as.character(unique(obj@colData[[idents]])) + message(paste0("Idents to loop: ", paste(vals_test, collapse = ", "))) + + } else if(mode == "one_vs_all"){ + if (is.null(vals_test)) { + vals_test <- as.character(unique(obj@colData[[contrast_var]])) + } else { + if (any(!vals_test %in% unique(obj@colData[[contrast_var]]))) { + stop("vals_test must be values in the contrast var") + } + } + } else { + stop("something wrong") + } + + + print("calc. TMM norm factors") + dge.l = calcNormFactors(obj, method = "TMM") + + res <- switch( + mode, + one_vs_all = pb_one_vs_all(dge_formula, dge.l, contrast_var, vals_test), + within = pb_within(dge_formula, dge.l, contrast_var, vals_test, idents, pval = pval) + ) + return(res) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA: Pseudobulk within +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pb_within <- function( + dge_formula, + dge.l, + contrast_var, + vals_test, + idents, + pval +) { + + # group_test = "CD4_CTL" + Reduce(rbind, lapply(vals_test, function(group_test) { + + message(group_test) + + dge.l.wrk = dge.l + dge.l.wrk = dge.l.wrk[, dge.l.wrk$samples[[idents]] %in% group_test] + dge.l.wrk$samples = droplevels(dge.l.wrk$samples) + + group = dge.l.wrk$samples[[contrast_var]] + keep.exprs = edgeR::filterByExpr(dge.l.wrk, group = contrast_var) + dge.l.wrk = dge.l.wrk[keep.exprs, ] + + ## Do DGE with Limma + model.matrix.check = try(model.matrix(dge_formula , data = dge.l.wrk$samples), silent = T) + if(inherits(model.matrix.check, "try-error") == F){ + design = model.matrix(dge_formula , data = dge.l.wrk$samples) + } else { + all_vars <- unlist( + strsplit(tail(as.character(dge_formula), 1), split = " \\+ ") + ) + message(paste0("model.matrix() throws an Error (Error in `contrasts). Removing coef: ", head(all_vars, 1))) + dge_formula <- as.formula( + paste0("~", paste(tail(all_vars, -1), collapse = "+")) + ) + design = model.matrix(dge_formula , data = dge.l.wrk$samples) + } + + colnames(design) = gsub(" et al.", "", colnames(design)) + + v = voom(dge.l.wrk, design, plot = F) + vfit = lmFit(v, design, method = "robust") + + # print(colnames(vfit$coefficients)) + contrast_name = grep( + paste0("^", gsub("\\+", "\\\\+", contrast_var)), colnames(vfit$coefficients), + value = TRUE + ) + # print(contrast_name) + + dgea.res = topconfects::limma_confects(vfit, coef = contrast_name, fdr=pval, full = T)$table + dgea.res = dgea.res %>% dplyr::rename(logFC = effect, adj.P.Val = fdr_zero) + dgea.res$PB_GROUP = group_test + dgea.res = rbind( + dgea.res[((!is.na(dgea.res$confect)) & (dgea.res$AveExpr > 0)), ], + dgea.res[is.na(dgea.res$confect), ] + ) + ctrst.lvls = levels(dge.l.wrk$samples[[contrast_var]]) + dgea.res$CTRST = paste0(ctrst.lvls[2], "_vs_", ctrst.lvls[1]) + dgea.res$GROUP1 = ctrst.lvls[2] + dgea.res$GROUP2 = ctrst.lvls[1] + message(paste0(contrast_var, " -> ", group_test, " -> ", table(dgea.res$adj.P.Val < pval)[2])) + + return(dgea.res) + })) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA: Pseudobulk one versus all +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pb_one_vs_all <- function( + dge_formula, + dge.l, + contrast_var, + vals_test +) { + + # foreground_id = "CD4_NaiveLike" + Reduce(rbind, lapply(vals_test, function(foreground_id) { + + message(foreground_id) + + dge.l.wrk = dge.l + + meta <- dge.l.wrk$samples + meta[[contrast_var]] <- factor( + ifelse(meta[[contrast_var]] == foreground_id, paste0("cluster_", foreground_id), "background") + ) + dge.l.wrk$samples = meta + + group = dge.l.wrk$samples[[contrast_var]] + keep.exprs = edgeR::filterByExpr(dge.l.wrk, group = group) + dge.l.wrk = dge.l.wrk[keep.exprs, ] + + ## Do DGE with Limma + design = model.matrix(dge_formula , data = dge.l.wrk$samples) + colnames(design) = gsub(" et al.", "", colnames(design)) + colnames(design) = gsub("PB_GROUP", "", colnames(design)) + + v = voom(dge.l.wrk, design, plot = F) + vfit = lmFit(v, design, method = "robust") + + contrast_name = grep( + paste0("cluster_", gsub("\\+", "\\\\+", foreground_id), "$"), colnames(vfit$coefficients), + value = TRUE + ) + + dgea.res = topconfects::limma_confects(vfit, coef = contrast_name, fdr=0.05, full = T)$table + dgea.res = dgea.res %>% dplyr::rename(logFC = effect, adj.P.Val = fdr_zero) + dgea.res$PB_GROUP = foreground_id + dgea.res = rbind( + dgea.res[((!is.na(dgea.res$confect)) & (dgea.res$AveExpr > 0)), ], + dgea.res[is.na(dgea.res$confect), ] + ) + ctrst.lvls = levels(dge.l.wrk$samples[[contrast_var]]) + dgea.res$CTRST = paste0(ctrst.lvls[2], "_vs_", ctrst.lvls[1]) + dgea.res$GROUP1 = ctrst.lvls[2] + dgea.res$GROUP2 = ctrst.lvls[1] + message(paste0(contrast_var, " -> ", foreground_id, " -> ", table(dgea.res$adj.P.Val < 0.05)[2])) + + return(dgea.res) + })) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# PCA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pca_plot = function(object, + intgroup = "condition", + pc1.pn = "group1", + pc2.pn = "group2", + pc1 = 1, + pc2 = 2, + ntop = 500, + type = NULL, + log = NULL, + point.size = 1.7) { + + library(ggthemes) + + if (class(object) == "DESeqTransform") { + object.exprs = assay(object) + object.pD = colData(object) + } + if (class(object) == "DESeqDataSet") { + object.exprs = assay(object) + object.pD = colData(object) + } + if (class(object) == "ExpressionSet") { + object.exprs = exprs(object) + object.pD = pData(object) + } + if (class(object) == "DGEList") { + object.exprs = object$counts + object.pD = object$samples + } + if (class(object) == "SummarizedExperiment") { + if (is.null(type)) { + object.exprs = assay(object) + } else { + object.exprs = assays(object)[[type]] + } + if (!is.null(log)) { + object.exprs = log(object.exprs) + } + object.pD = colData(object) + } + + + # calculate the variance for each gene + rv = rowVars(object.exprs) + # select the ntop genes by variance + select = order(rv, decreasing = TRUE)[seq_len(min(ntop, length(rv)))] + # perform a PCA on the data in assay(x) for the selected genes + pca = prcomp(t(object.exprs[select, ])) + # the contribution to the total variance for each component + percentVar = pca$sdev ^ 2 / sum(pca$sdev ^ 2) + + if (!all(intgroup %in% names(object.pD))) { + stop("the argument 'intgroup' should specify columns of the pheno table") + } + + intgroup.df = as.data.frame(object.pD[, intgroup, drop = FALSE]) + + # assembly the data for the plot + d = data.frame( + PC1 = pca$x[, 1], + PC2 = pca$x[, 2], + PC3 = pca$x[, 3], + PC4 = pca$x[, 4], + PC4 = pca$x[, 5], + intgroup.df, + name = colnames(object) + ) + + if (length(intgroup) == 1) { + pca1 = + ggplot(data = d, aes( + x = d[, eval(pc1)], + y = d[, eval(pc2)], + color = .data[[intgroup[1]]] + )) + geom_point(size = point.size) + pca1 = pca1 + labs(colour = pc1.pn) + } else { + pca1 = ggplot(data = d, + aes(x = d[, eval(pc1)], y = d[, eval(pc2)], color = .data[[ intgroup[1] ]], shape = .data[[ intgroup[2] ]])) + + geom_point(size = point.size) + pca1 = pca1 + guides(color = guide_legend(order = 1), shape = guide_legend(order = 2)) + pca1 = pca1 + labs(colour = pc1.pn, shape = pc2.pn) + } + pca1 = pca1 + xlab(paste0("PC", eval(pc1), ": ", round(percentVar[eval(pc1)] * 100), "% var")) + pca1 = pca1 + ylab(paste0("PC", eval(pc2), ": ", round(percentVar[eval(pc2)] * 100), "% var")) + pca1 = pca1 + + if (is.numeric(d[, intgroup[1]])) { + pca1 + } else { + if (length(levels(d[, intgroup[1]])) > 4) { + pca1 = pca1 + scale_colour_manual(values = ggthemes::tableau_color_pal("Tableau 20")(20), na.value = "black") + } else { + pca1 = pca1 + scale_colour_manual(values = ggthemes::tableau_color_pal("Tableau 10")(10), na.value = "black") + } + pca1 + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Bubble plot for cell identity markers +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +mrkr_bubble = function( + dgea.res = NULL, + se.obj = NULL, + group = "PB_GROUP", + effect.size = "confect", + positive.effect.size = T, + order.by.effect.size = T, + nbr.tops = 5, + quantile.cut = .99, + exprs.assay = "log.cpm", + features = NULL, + font.size = 10, + aspectRatio = 2, + pt.size = 1 +) { + + library(scico) + library(circlize) + library(ComplexHeatmap) + +if(is.null(features)) { + # filter for significant regulated genes + dge.res = dgea.res[!is.na(dgea.res$confect), ] + + # dge.res = dge.res[!dge.res$name %in% unique(dge.res$name[duplicated(dge.res$name)]), ] + + if (positive.effect.size == T) { + dge.res = dge.res[dge.res[[effect.size]] > 0, ] + } + + if (order.by.effect.size == T) { + dge.res = dge.res %>% arrange(!!as.name(group), desc(abs(!!as.name(effect.size)))) + } else { + dge.res = dge.res %>% dplyr::arrange(!!as.name(group), adj.P.Val) + } + + tops = dge.res %>% + group_by(!!as.name(group)) %>% + slice_head(n = nbr.tops) %>% data.frame() + tops = tops$name + + ct.keep = unique(as.character(dge.res[[group]])) + se.obj = se.obj[, se.obj[[group]] %in% ct.keep] + colData(se.obj) = droplevels(colData(se.obj)) + +} else { + tops = features +} + + + + mat = assays(se.obj)[[exprs.assay]][unique(tops), ] + colnames(mat) = gsub("\\_.+", "", colnames(mat)) + mat = limma::avearrays(mat) + mat = t(scale(t(mat))) + df = reshape2::melt(mat) + colnames(df) = c("GENE", "CT", "AVE") + + v.max = quantile(df$AVE, quantile.cut) + df$AVE[abs(df$AVE) > v.max] = v.max + + df$CT = gsub("\\.", " ", df$CT) + + ggplot(df, aes(x = CT,y = GENE)) + + geom_point(aes(color = AVE), size = pt.size) + + scale_color_scico(palette = "vik", midpoint = 0, limits = c(-v.max,v.max)) + + mytheme(base_size = font.size) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor = element_blank(), + panel.spacing = unit(.5, "lines"), + axis.text.x = element_text(angle=45, hjust=1, vjust = 1), + axis.text.y = element_text(size = rel(1)), + axis.title.x = element_blank(), + axis.title.y = element_blank(), + axis.ticks.x = element_blank(), + aspect.ratio = aspectRatio + ) + + guides( + size="none", + color = guide_colorbar( + title = "Z-scored\nAverage\nExpression", + title.hjust = 0, barwidth = unit(.5, 'lines'), barheight = unit(5, 'lines') + ) + ) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# SVA +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +sva_cor = function(obj) { + + dds = estimateSizeFactors(DESeq2::DESeqDataSetFromMatrix(assay(obj), colData(obj), ~ 1)) + assays(obj)$norm = log2(DESeq2::counts(dds, normalized = T) + 1 ) + for (i in unique(obj$PB_GROUP)) { + se.tmp = obj[, obj$PB_GROUP == i] + colData(se.tmp) = droplevels(colData(se.tmp)) + + obj.sva = se.tmp + mod = model.matrix(~ RESPONSE_CONSENSUS, colData(obj.sva)) + mod0 = model.matrix(~ 1, colData(obj.sva)) + + obj.sva = obj.sva[rowSums(assays(obj.sva)$counts > 5) > ceiling(ncol(obj.sva) / 100 * 10),] + + # n.sv = num.sv(assays(obj.sva)$norm, mod) + n.sv = 3 + svseq = svaseq(assays(obj.sva)$norm, mod, mod0, n.sv = n.sv) + colnames(svseq$sv) = paste0("SV_", seq(1, n.sv)) + + head(pdata.sva) + pdata.sva = data.frame(colData(obj.sva)) %>% dplyr::select(PRODUCT, SEX, AGE) + + rcorr_prep = function(svseq, df) { + sva_corr = as.data.frame(cbind(svseq$sv, df)) + for (i in colnames(sva_corr)) { + if (!is.numeric(sva_corr[[i]])) { + sva_corr[[i]] = fct_na_value_to_level(sva_corr[[i]], "NONE") + } + } + + for (i in seq_len(ncol(sva_corr))){ + if (is.numeric(sva_corr[,i]) == FALSE){ + droplevels(sva_corr[,i]) + sva_corr[,i] = as.numeric(sva_corr[,i]) + } + } + return(sva_corr) + } + + sva_corr = rcorr_prep(svseq, pdata.sva) + + corrplot( + cor(as.matrix(sva_corr)), + addCoef.col = "black", + mar = c(0, 0, 1, 0), + title = i, + diag = F, + type = "upper", + number.cex=.7, + tl.cex = .7, + cl.cex = .7, + cl.ratio = 0.1, + tl.srt=45, + tl.col = "black", + insig = "blank" + ) + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +getvolcone=function( + res_table, + pcutoff=0.05, + pval.col = "adj.P.Val", + es.col = "logFC", + group = "PB_GROUP", + FCcutoff=log2(1.5), + ftrs.col = "name" +){ + + p=EnhancedVolcano( + res_table, + lab = res_table[[ftrs.col]], + x = es.col, + y = pval.col, + title=res_table[[group]][1], + subtitle =NULL, + pCutoff = pcutoff, + FCcutoff = FCcutoff, + legendPosition = 'none', + ylim = c(0, max(max(-log10(res_table[[pval.col]]), na.rm = TRUE) + 1, -log10(0.05)+1)), + caption=NULL, + pointSize = 0.5, + axisLabSize = 10, + titleLabSize = 10, + legendLabSize = 10, + labSize = 3 + ) +} \ No newline at end of file diff --git a/code/helper/functions.R b/code/helper/functions.R new file mode 100644 index 0000000..3ec3e11 --- /dev/null +++ b/code/helper/functions.R @@ -0,0 +1,675 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Normalize, Harmony, Clustering +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +integration = function( + obj, + obj.l = NULL, + no.ftrs = 500, + .assay = "RNA", + max.cl = 1, + min.cells.per.sample = 25, + threads = 5, + .nbr.dims = 15, + do.cluster = T, + do.dimreduc = T, + cc.regr = F, + hvg.union = T, + custom.features = NULL, + run.integration = T, + harmony.group.vars = NULL, + obj.split.by = "orig.ident", + .perp = 50, + dmreduc.dims = NULL, + n.neighbors = 30, + min.dist = 0.3) { + + library(SignatuR) + library(parallel) + library(BiocParallel) + + start.time <- Sys.time() + + if(is.null(dmreduc.dims)) { + dmreduc.dims = .nbr.dims + } + + obj@meta.data = droplevels(obj@meta.data) + obj = DietSeurat(obj, counts = TRUE, data = TRUE) + obj = NormalizeData(obj) + + # Gene categories to exclude from variable genes + bl <- c( + SignatuR::GetSignature(SignatuR$Hs$Compartments$Mito), + SignatuR::GetSignature(SignatuR$Hs$Compartments$TCR), + SignatuR::GetSignature(SignatuR$Hs$Compartments$Immunoglobulins) + ) + bl <- unique(bl) + + if (hvg.union == T) { + + if(is.null(obj.l)) { + print("Split object") + obj.l = Split_Object(obj, split.by = obj.split.by, threads = threads) + } + + select.bool = unlist(lapply(obj.l, function(x){ncol(x) >= min.cells.per.sample})) + print(table(select.bool)) + obj.l = obj.l[select.bool] + length(obj.l) + + print("HVG") + obj.l = parallel::mclapply(obj.l, function(x) { + x = x[!rownames(x) %in% bl, ] + x = FindVariableFeatures(x, selection.method = "vst", assay = .assay, verbose = FALSE) + x + }, mc.cores = threads) + + features = SelectIntegrationFeatures(object.list = obj.l, nfeatures = no.ftrs) + VariableFeatures(obj) = features + rm(obj.l); gc() + + } else if (!is.null(custom.features)) { + VariableFeatures(obj) = custom.features + } else { + obj = FindVariableFeatures(obj, selection.method = "vst", nfeatures = no.ftrs, assay = .assay) + } + + if (cc.regr == T) { + # obj = ScaleData(obj, vars.to.regress = c("S.Score", "G2M.Score"), assay = .assay) + obj = ScaleData(obj, vars.to.regress = c("Perc_of_mito_genes"), assay = .assay) + } else { + obj = ScaleData(obj, assay = .assay) + } + + obj = RunPCA(obj, assay = .assay) + plot(ElbowPlot(obj, ndims = 50)) + + print(paste0("### PCs used for harmony clustering and dim reduc: ", .nbr.dims, " ###")) + + if(run.integration == T){ + obj = RunHarmony( + obj, group.by.vars = harmony.group.vars, # theta = c(2,3), + reduction='pca', assay = .assay, dims.use = 1:.nbr.dims, max.iter.harmony = 15) + comp.wrk = 'harmony' + } else { + comp.wrk = 'pca' + } + + if (do.cluster == T) { + print("Find clusters") + obj = FindNeighbors(obj, reduction = comp.wrk, dims = 1:.nbr.dims) + + reso = seq(0,max.cl,.1) + names(reso) = reso + findclusters.res = parallel::mclapply(reso, function(x) { + FindClusters(obj, resolution = x)@meta.data[, "seurat_clusters", drop = F] + }, mc.cores = length(reso)) + res.names = names(findclusters.res) + findclusters.res = do.call("cbind", findclusters.res) + colnames(findclusters.res) = paste0("RNA_snn_res.", res.names) + stopifnot(identical(rownames(obj@meta.data), rownames(findclusters.res))) + obj = AddMetaData(obj, findclusters.res) + # obj = FindClusters(obj, resolution = seq(0,max.cl,.1)) + } + if (do.dimreduc == T) { + # print("tSNE") + # obj = RunTSNE( + # obj, reduction = comp.wrk, dims = 1:dmreduc.dims, seed.use = 1234, + # tsne.method = "FIt-SNE", nthreads = threads, perplexity = .perp + # ) + print("UMAP") + obj = RunUMAP( + obj, reduction = comp.wrk, dims = 1:dmreduc.dims, seed.use = 1234, + min.dist = min.dist, n.neighbors = n.neighbors + ) + } + + end.time <- Sys.time() + time.taken <- end.time - start.time + print(time.taken) + + return(obj) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# WNN analysis of RNA and ADT +# https://satijalab.org/seurat/articles/weighted_nearest_neighbor_analysis.html +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +integraton_wnn = function( + se.obj = NULL, + dims.use.adt = 15, + dims.use.rna = 25 +){ + + DefaultAssay(se.obj) = "ADT" + + VariableFeatures(se.obj) <- rownames(se.obj[["ADT"]])[!rownames(se.obj[["ADT"]]) %in% c("FITC", "PE", "TCRV2", "HASHTAG-1", "HASHTAG-2")] + + se.obj = NormalizeData(se.obj, normalization.method = 'CLR', margin = 2) %>% + ScaleData() %>% + RunPCA(reduction.name = 'apca', approx = F) + + se.obj = RunHarmony( + se.obj, group.by.vars = "orig.ident", reduction='apca', assay = "ADT", + dims.use = 1:dims.use.adt, max.iter.harmony = 15, + reduction.save = "aharmony" + ) + + se.obj = RunUMAP( + se.obj, reduction = "aharmony", reduction.name = 'adt.umap', assay = 'ADT', + dims = 1:dims.use.adt, seed.use = 1234 + ) + + se.obj <- FindMultiModalNeighbors( + se.obj, reduction.list = list("harmony", "aharmony"), modality.weight.name = "RNA.weight", + dims.list = list(1:dims.use.rna, 1:dims.use.adt) + ) + + # se.obj = RunSPCA(se.obj, assay = "RNA", graph = "wsnn", npcs = 20) + se.obj <- RunUMAP( + se.obj, nn.name = "weighted.nn", reduction.name = "wnn.umap", reduction.key = "wnnUMAP_", + dims = 1:dims.use.rna, seed.use = 1234 + ) + DefaultAssay(se.obj) = "RNA" + se.obj +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Presto: Wilcoxon Test +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +run_wilx = function( + obj = NULL, + target = NULL, + contrast.group = NULL, + contrast = NULL, + min.cells = 10, + slot = "data", + assay = "RNA", + rm.var.chains = T, + padj.thresh = 0.05, + lfc.thresh = 1.5 +){ + + DefaultAssay(obj) = assay + + if(assay == "ADT") { + obj = obj[!rownames(obj[["ADT"]]) %in% c("FITC", "PE"), ] + } + + obj = DietSeurat( + obj, + counts = TRUE, + data = T, + scale.data = FALSE, + features = NULL, + assays = assay, + dimreducs = F, + graphs = F + ) + + obj@meta.data = droplevels(obj@meta.data) + obj@meta.data$RM = is.na(obj@meta.data[[target]]) + obj = subset(obj, subset = RM == F) + obj@meta.data = droplevels(obj@meta.data) + obj.l = Seurat::SplitObject(obj, split.by = target) + + tbl = table(obj@meta.data[[contrast.group]], obj@meta.data[[target]]) + # Filter out celltypes with less than x cells in one group + obj.l = obj.l[colnames(tbl)[colSums(tbl >= min.cells) == 2]] + + t = names(obj.l) + options(warn = 1) + bpparam = BiocParallel::MulticoreParam(workers = length(t)) + wil.res = BiocParallel::bplapply(t, function(x) { + + markers = presto::wilcoxauc( + obj.l[[x]], group_by = contrast.group, + seurat_assay = assay, assay = slot, + groups_use = c(contrast) + ) + markers$cluster = x + markers = subset(markers, group == contrast[1]) + markers$padj = p.adjust(markers$padj, method = "bonferroni") + rownames(markers) = paste0(markers$feature, "_", markers$cluster) + markers + + }, BPPARAM = bpparam) + + wil.res = do.call("rbind", wil.res) + if (rm.var.chains == T) { + wil.res = wil.res[!grepl('^IGHV|^IGK|^IGL|^IGL|^TR(B|A)V|JCHAIN', wil.res$feature), ] + } + wil.res = wil.res[order(abs(wil.res$logFC), decreasing = T), ] + wil.res$significant = (wil.res$padj < padj.thresh) & (abs(wil.res$logFC) > log(lfc.thresh)) + wil.res +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Presto: Wilcoxon Test: Cell identity marker +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +run_wilx_ct_marker = function( + obj = NULL, + target = NULL, + min.cells = 10, + slot = "data", + assay = "RNA", + downsample = F, + cells = 1000, + padj.thresh = 0.05, + lfc.thresh = 1.5 +){ + + DefaultAssay(obj) = assay + + obj = DietSeurat( + obj, + counts = TRUE, + data = T, + scale.data = FALSE, + features = NULL, + assays = assay, + dimreducs = F, + graphs = F + ) + + obj@meta.data = droplevels(obj@meta.data) + obj@meta.data$RM = is.na(obj@meta.data[[target]]) + obj = subset(obj, subset = RM == F) + obj@meta.data = droplevels(obj@meta.data) + + if(downsample == T) { + Idents(obj) = target + obj = subset(obj, downsample = cells) + } + + t = unique(as.character(obj@meta.data[[target]])) + bpparam = BiocParallel::MulticoreParam(workers = length(t)) + wil.res = BiocParallel::bplapply(t, function(x) { + + obj@meta.data$contrast = ifelse(obj@meta.data[[target]] == x, x, "other") + markers = presto::wilcoxauc( + obj, group_by = "contrast", + seurat_assay = assay, assay = slot, + groups_use = c(x, "other") + ) + + markers = subset(markers, group == x) + markers$padj = p.adjust(markers$padj, method = "bonferroni") + rownames(markers) = markers$feature + markers + + }, BPPARAM = bpparam) + + wil.res = do.call("rbind", wil.res) + wil.res = wil.res[order(abs(wil.res$logFC), decreasing = T), ] + wil.res$significant = (wil.res$padj < padj.thresh) & (abs(wil.res$logFC) > log(lfc.thresh)) + wil.res +} + + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Relevel (for plots) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +pheno_finetuning = function(obj) { + + if(class(obj) == "Seurat") { + pd.df = obj@meta.data + } else { + pd.df = obj + } + + pd.df$TIMEPOINT = factor( + pd.df$TIMEPOINT, + levels = c("Pre", "Wk 3 to 4", "Mo 3 to 6") + ) + + pd.df$RESPONSE_CONSENSUS = factor( + pd.df$RESPONSE_CONSENSUS, + levels = c("CR", "nonCR") + ) + + pd.df$GROUP = factor( + pd.df$GROUP, levels = c("Pre-infusion", "Post-infusion") + ) + + pd.df = pd.df %>% + dplyr::mutate_if(is.character, as.factor) + + pd.df = droplevels(pd.df) + + if(class(obj) == "Seurat") { + stopifnot(identical(rownames(pd.df), rownames(obj@meta.data))) + obj@meta.data = pd.df + obj + } else { + pd.df + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Add column in metadata wether CD4/CD8, CAR are present +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +cd4cd8_car_present = function( + obj +){ + cd8cd4 = FetchData(obj, c("CD8A", "CD8B", "CD4"), slot = "counts") + ct.cd8cd4 = cd8cd4 %>% mutate( + CD4CD8_BY_EXPRS = case_when( + CD4 > 0 & CD8A == 0 & CD8B == 0 ~ "CD4+CD8-", + CD4 == 0 & (CD8A > 0 | CD8B > 0) ~ "CD4-CD8+", + CD4 == 0 & CD8A == 0 & CD8B == 0 ~ "CD4-CD8-", + CD4 > 0 & (CD8A > 0 | CD8B > 0) ~ "CD4+CD8+", + TRUE ~ "unresolved" + )) %>% + dplyr::select(CD4CD8_BY_EXPRS) + rownames(ct.cd8cd4) = rownames(cd8cd4) + obj = AddMetaData(obj, ct.cd8cd4) + + cd3 = FetchData(obj, c("CD3D", "CD3E", "CD3G"), slot = "counts") + cd3 = cd3 %>% mutate( + CD3_BY_EXPRS = case_when( + CD3D > 0 | CD3E > 0 | CD3G > 0 ~ "CD3", + TRUE ~ "unresolved" + )) %>% + dplyr::select(CD3_BY_EXPRS) + obj = AddMetaData(obj, cd3) + + if("CAR-BCMA" %in% rownames(GetAssayData(obj, slot = c("counts"), assay = "RNA"))){ + car.ftr = FetchData(obj, c("CAR-BCMA"), slot = "counts") + obj$CAR_BY_EXPRS = as.factor(car.ftr$`CAR-BCMA` > 0) + } else { + obj$CAR_BY_EXPRS = FALSE + obj$CAR_BY_EXPRS = as.factor(obj$CAR_BY_EXPRS) + } + + obj +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Track number of cell (pre-processing +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +count_cells_per_sample = function(obj = NULL, count.base = NULL, col.name = NULL){ + + l = lapply(obj, function(x){ + x@meta.data %>% dplyr::select(STUDY, orig.ident) + }) + df = do.call("rbind", l) + df = df %>% dplyr::count(STUDY, orig.ident) + if(is.null(count.base)) { + return(df) + } else { + count.base[[col.name]] = df$n[match(count.base$orig.ident, df$orig.ident)] + return(count.base) + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# get metadata from Seurat +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +get_metadata <- function(obj, ..., embedding = names(obj@reductions)) { + + res = as_tibble(obj@meta.data, rownames = "cell") + + if (!is.null(embedding)) { + if (any(!embedding %in% names(obj@reductions))) { + stop(paste0(embedding, " not found in seurat object\n"), call. = FALSE) + } + embed_dat = purrr::map(names(obj@reductions), ~obj@reductions[[.x]]@cell.embeddings[, 1:2]) %>% + do.call(cbind, .) %>% + as.data.frame() %>% + tibble::rownames_to_column("cell") + + res = dplyr::left_join(res, embed_dat, by = "cell") + } + + if (length(list(...)) > 0) { + cols_to_get <- setdiff(..., colnames(obj@meta.data)) + if (length(cols_to_get) > 0) { + res = Seurat::FetchData(obj, vars = cols_to_get) %>% + tibble::rownames_to_column("cell") %>% + dplyr::left_join(res, ., by = "cell") + } + } + res +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Split Seurat object (BiocParallel) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +Split_Object = function(object, split.by = "orig.ident", threads = 5) { + + library(parallel) + library(BiocParallel) + + groupings <- FetchData(object = object, vars = split.by)[, 1] + groupings <- unique(x = as.character(x = groupings)) + names(groupings) = groupings + + if (is.null(threads)) { + bpparam = BiocParallel::MulticoreParam(workers = length(groupings)) + } else { + bpparam = BiocParallel::MulticoreParam(workers = threads) + } + + obj.list = BiocParallel::bplapply(groupings, function(grp) { + cells <- which(x = object[[split.by, drop = TRUE]] == grp) + cells <- colnames(x = object)[cells] + se = subset(x = object, cells = cells) + se@meta.data = droplevels(se@meta.data) + se + }, BPPARAM = bpparam) + + return(obj.list) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Modified from: https://github.com/carmonalab/UCell +# Modified: Averaging can be weighted or unweighted +# Modified: Return Seurat obj or data.frame +# Single-cell data are sparse. It can be useful to 'impute' scores by neighboring +# cells and partially correct this sparsity. The new function SmoothKNN performs +# smoothing of single-cell signature scores by weighted average of the k-nearest +# neighbors in a given dimensionality reduction. +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +knn_scores <- function( + matrix=NULL, + nn=NULL, + do.smooth = F +) { + sig.cols <- colnames(matrix) + + if(do.smooth == T) { + w.df <- vapply(sig.cols, FUN.VALUE=numeric(nrow(matrix)), FUN=function(s) { + ss.scores <- matrix[,s] + weighted.scores <- vapply( + X = seq_len(nrow(nn$index)), + FUN.VALUE = numeric(1), + FUN = function(x) { + r <- nn$index[x,] + r <- c(x,r) + + d <- nn$distance[x,] + d <- c(d[1],d) + + w <- 1/(0.01+d) + + sum(w * ss.scores[r])/sum(w) + }) + }) + } else { + w.df <- vapply(sig.cols, FUN.VALUE=numeric(nrow(matrix)), FUN=function(s) { + ss.scores <- matrix[,s] + weighted.scores <- vapply( + X = seq_len(nrow(nn$index)), + FUN.VALUE = numeric(1), + FUN = function(x) { + r <- nn$index[x,] + r <- c(x,r) + mean(ss.scores[r]) + }) + }) + } + + rownames(w.df) <- rownames(matrix) + as.data.frame(w.df) +} + +SmoothKNN.Seurat <- function( + obj=NULL, + signature.names=NULL, + reduction="pca", + k=10, + BNPARAM=AnnoyParam(), + BPPARAM=SerialParam(), + suffix="_kNN", + assay=NULL, + slot="data", + knn_smooth = F, + return.seurat = F +) { + + .cran_packages = c("Seurat", "Matrix") + .inst = .cran_packages %in% installed.packages() + if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") + } + + .bioc_packages = c("BiocNeighbors", "BiocParallel") + .inst <- .bioc_packages %in% installed.packages() + if (any(!.inst)) { + library(BiocManager) + BiocManager::install(.bioc_packages[!.inst], ask = T) + } + + list.of.packages = c(.cran_packages, .bioc_packages) + + ## Loading library + for (pack in list.of.packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) + } + + if (!reduction %in% Seurat::Reductions(obj)) { + stop(sprintf("Could not find reduction %s in this object", reduction)) + } + if (is.null(signature.names)) { + stop("Please provide the metadata column names that you want to smooth") + } + + if (is.null(assay)) { # Work on metadata + found <- intersect(signature.names, colnames(obj[[]])) + notfound <- setdiff(signature.names, found) + + if (length(found)==0) { + stop("Could not find any of the given signatures in this object") + } + if (length(notfound)>0) { + nf <- paste(notfound, collapse=",") + mess <- sprintf("The following signature were found in metadata:\n* %s",nf) + warning(mess, immediate.=TRUE, call.=FALSE, noBreaks.=TRUE) + } + m <- obj[[found]] + } else { # Work directly on features + + exp <- Seurat::GetAssayData(obj, assay=assay, slot=slot) + feats <- rownames(exp) + + found <- intersect(signature.names, feats) + notfound <- setdiff(signature.names, found) + + if (length(found)==0) { + stop("Could not find any of the given features in this object") + } + if (length(notfound)>0) { + nf <- paste(notfound, collapse=",") + mess <- sprintf("The following features were not found in assay %s:\n* %s", + assay, nf) + warning(mess, immediate.=TRUE, call.=FALSE, noBreaks.=TRUE) + } + m <- Matrix::t(exp[found, , drop=FALSE]) + } + ncells <- ncol(obj) + + + # Find kNNs + space <- Seurat::Embeddings(obj, reduction=reduction) + nn <- BiocNeighbors::findKNN(space, k=k, BNPARAM=BNPARAM, BPPARAM=BPPARAM) + + # Do smoothing (or not) + smooth.df <- knn_scores(matrix=m, nn=nn, do.smooth = knn_smooth) + + if(return.seurat == T) { + if (is.null(assay)) { #metadata + colnames(smooth.df) <- paste0(colnames(smooth.df), suffix) + obj <- Seurat::AddMetaData(obj, metadata = smooth.df) + } else { #new assay + nas <- paste0(assay, suffix) + obj[[nas]] <- Seurat::CreateAssayObject(data=t(smooth.df)) + } + return(obj) + } else { + return(smooth.df) + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# flag levels of significance +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +add_signif <- function( + data, p.col = NULL, output.col = NULL, + cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 1), + symbols = c("****", "***", "**", "*", "ns"), + pval.relax = F +){ + + if(pval.relax == T) { + cutpoints = c(0, 0.0001, 0.001, 0.01, 0.05, 0.1, 1) + symbols = c("*****", "****", "***", "**", "*", "") + } + + if(is.null(output.col)) { + output.col <- paste0(p.col, ".signif") + } + .p.values <- data %>% pull(!!p.col) + if(all(is.na(.p.values))) { + .p.signif <- rep("", length(.p.values)) + } + else{ + .p.signif <- .p.values %>% + stats::symnum(cutpoints = cutpoints, symbols = symbols, na = "") %>% + as.character() + } + data %>% + dplyr::mutate(!!output.col := .p.signif) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Write DGEA results to xlsx +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +write_to_xlsx = function( + df = NULL, + sheet = "sheet", + filename = "Supplementaltable3.xlsx", + wb = NULL +) { + library("openxlsx") + + if(is.null(wb)){ + wb <- createWorkbook() + } + + addWorksheet(wb, sheet) + writeData( + wb, sheet, + df %>% dplyr::select( + "Gene_symbol" = feature, "LogFC" = logFC, + "Pvalue" = pval, "FDR" = padj, "Cell_identity" = cluster + ) %>% + dplyr::arrange(Cell_identity, desc(LogFC)), + startRow = 1, startCol = 1) + saveWorkbook(wb, filename, overwrite = T) +} \ No newline at end of file diff --git a/code/helper/functions_plots.R b/code/helper/functions_plots.R new file mode 100644 index 0000000..23dc423 --- /dev/null +++ b/code/helper/functions_plots.R @@ -0,0 +1,1600 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Violin plot (pre filtering) with cutoffs +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +qc_vln_plot_cell = function( + obj = se.meta, + .features = "nFeature_RNA", + .group.by = "orig.ident", + .split.by = "STUDY", + plot_title = "Genes Per Cell", + x_axis_label = NULL, + y_axis_label = "Features", + low_cutoff = NULL, + high_cutoff = NULL +){ + library(ggplot2) + library(ggthemes) + + if(!is.list(obj)) { + df = obj@meta.data %>% dplyr::select(.data[[.group.by]], .data[[.features]], .data[[.split.by]]) + df + } + if(class(obj) == "list") { + l = lapply(obj, function(x){ + df = x@meta.data %>% dplyr::select(.data[[.group.by]], .data[[.features]], .data[[.split.by]]) + }) + df = do.call("rbind", l) + df + } + if(class(obj) == "data.frame") { + df = obj %>% dplyr::select(.data[[.group.by]], .data[[.features]], .data[[.split.by]]) + df + } + + ggplot(data = df, mapping = aes(x = .data[[.group.by]], y = .data[[.features]], groups = .data[[.split.by]])) + + geom_violin( + size = .1, + width = 1, + scale = "area", + na.rm = TRUE + ) + + stat_summary( + fun.min = function(z) { quantile(z,0.25) }, + fun.max = function(z) { quantile(z,0.75) }, + fun = median, colour = "#0077BB", size = .2) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank(), + legend.position = "bottom", + plot.title = element_text(size = rel(1.2)) + ) + + facet_grid(. ~ .data[[.split.by]], scales = "free", space='free') + + geom_hline(yintercept = c(low_cutoff, high_cutoff), linetype = "dashed", color = "#BB5566", size = .3) + + xlab(x_axis_label) + + ylab(y_axis_label) + + ggtitle(plot_title) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +count_cells_per_sample = function(obj = NULL, count.base = NULL, col.name = NULL){ + + l = lapply(obj, function(x){ + x@meta.data %>% dplyr::select(STUDY, orig.ident) + }) + df = do.call("rbind", l) + df = df %>% dplyr::count(STUDY, orig.ident) + if(is.null(count.base)) { + return(df) + } else { + count.base[[col.name]] = df$n[match(count.base$orig.ident, df$orig.ident)] + return(count.base) + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Boxplot: Median values per sample (post filtering) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +qc_median_plot = function( + obj = se.meta, + .var = "nFeature_RNA", + .dotsize = 1, + .group_by = "TIMEPOINT_WK_MO_BINNED", + .color_by = "STUDY", + plot_title = "Median Genes/Cell per Sample", + y_axis_label = "Median Genes", + y.max = NA +){ + + library(Seurat) + library(ggplot2) + library(ggthemes) + library(scCustomize) + library(dplyr) + + calc.stat.tbl = function(se.obj) { + if(.var == "cells_per_sample") { + stat.tbl = table(se.obj@meta.data[["orig.ident"]]) %>% + data.frame() %>% + dplyr::rename(!!"orig.ident" := .data[["Var1"]], Number_of_Cells = .data[["Freq"]]) + } else { + stat.tbl <- scCustomize::Median_Stats(se.obj, "orig.ident", median_var = .var, default_var = FALSE) %>% + dplyr::slice(-n()) %>% + droplevels() + } + pheno = se.obj@meta.data[!duplicated(se.obj$orig.ident), ] + stat.tbl[[.group_by]] = pheno[[.group_by]][match(stat.tbl$orig.ident, pheno$orig.ident)] + stat.tbl[[.color_by]] = pheno[[.color_by]][match(stat.tbl$orig.ident, pheno$orig.ident)] + stat.tbl + } + + if(!is.list(obj)) { + df = calc.stat.tbl(obj) + } + + if(class(obj) == "list") { + l = lapply(obj, function(x){ + calc.stat.tbl(x) + }) + df = do.call("rbind", l) + } + + if(class(obj) == "data.frame") { + if(.var == "cells_per_sample") { + stat.tbl = table(obj[["orig.ident"]]) %>% + data.frame() %>% + dplyr::rename(!!"orig.ident" := .data[["Var1"]], Number_of_Cells = .data[["Freq"]]) + } else { + stat.tbl <- obj %>% group_by(.data[["orig.ident"]]) %>% + summarise_at(vars(one_of(.var)), median) + colnames(stat.tbl) <- c("orig.ident", paste0("Median_", .var)) + } + + pheno = obj[!duplicated(obj), ] + stat.tbl[[.group_by]] = pheno[[.group_by]][match(stat.tbl$orig.ident, pheno$orig.ident)] + stat.tbl[[.color_by]] = pheno[[.color_by]][match(stat.tbl$orig.ident, pheno$orig.ident)] + df = stat.tbl + } + + if (.group_by == "TIMEPOINT_WK_MO") { + df$TIMEPOINT_WK_MO = factor( + df$TIMEPOINT_WK_MO, + levels = c( + "Wk-5", "Wk-1", "D0", "IP", "Wk+1", "Wk+2", "Wk+3", "Wk+4", "Wk+6", "Wk+8", + "Mo+3", "Mo+4", "Mo+6" + ) + ) + } + + ggplot(data = df, mapping = aes(x = .data[[.group_by]], y = .data[[colnames(df)[2]]], fill = .data[[.color_by]])) + + geom_boxplot(fill = "white", outlier.colour = NA, lwd = .3) + + geom_dotplot(binaxis ='y', stackdir = 'center', dotsize = .dotsize, colour = NA) + + scale_fill_manual(values = ggthemes::tableau_color_pal("Tableau 10")(10)) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor = element_blank(), + ) + + ggtitle(plot_title) + + ylab(y_axis_label) + + xlab("") + + scale_y_continuous(expand = c(0, 0), limits = c(0, y.max)) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Dimension reduction: for features (assay) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dimreduc_features = function( + .obj = NULL, + features = NULL, + .reduc = "tsne", + pl.points = F, + pt.size = .3, + plot.grid = T, + base.size = 8, + .x.title = "UMAP 1", + .y.title = "UMAP 2", + .leg.title = NULL, + .title.size = 1, + .assay = "RNA", + log.scale.counts = F, + .quantile.fltr = T, + .na_cutoff = NULL, + min.max = NULL, + legend.wh = c(.3, 4), + order = T, + .raster.scattermore = FALSE, + .raster.scattermore.pixel = c(512,512), + .raster.scattermore.pointsize = 0, + .raster = FALSE, + .raster.dpi = 150, + .colors = rev(MetBrewer::met.brewer("Hokusai1",n=100)) +) { + + DefaultAssay(.obj) = .assay + + library(scales) + library(cowplot) + library(scico) + + if(!is.null(.x.title) & !is.null(.y.title)){ + if(grepl("sne", .reduc, ignore.case = T)) {.x.title = "tSNE 1"; .y.title = "tSNE 2"} + if(grepl("umap", .reduc, ignore.case = T)) {.x.title = "UMAP 1"; .y.title = "UMAP 2"} + } + + features = features[features %in% rownames(.obj)] + if(length(features) == 0) { stop("None of the genes are present in the data set")} + + if(log.scale.counts == T){ + exprs.sub = .obj@assays[[.assay]]@counts[features, , drop = F] + exprs.sub = log10(exprs.sub + 1) + } else { + exprs.sub = .obj@assays[[.assay]]@data[features, , drop = F] + } + + ftr.pl = list() + for (i in 1:length(features)) { + ftr = features[i] + reduc = data.frame(.obj@reductions[[.reduc]]@cell.embeddings) + colnames(reduc) = c("DIM_1", "DIM_2") + reduc$EXPRS = exprs.sub[ftr, ] + + if(order == T) { + reduc = reduc[order(reduc$EXPRS, decreasing = F), ] # For ggplot: highest values on the top + } else { + set.seed(1234) + reduc = reduc %>% dplyr::sample_frac(1L, replace = FALSE) # permute rows randomly + } + + ftr.exprs = reduc$EXPRS + + if(.quantile.fltr) { + qu.max = quantile(ftr.exprs[ftr.exprs > 0], .999) + ftr.exprs[ftr.exprs > qu.max] = qu.max + reduc$EXPRS = ftr.exprs + } + + if(!is.null(min.max)) { + e.min = min.max[1] + e.max = min.max[2] + } else if(!is.null(.na_cutoff) & is.null(min.max)) { + e.min = min(ftr.exprs[ftr.exprs > .na_cutoff]) + e.max = max(ftr.exprs) + } else if (is.null(.na_cutoff) & is.null(min.max)) { + e.min = min(ftr.exprs) + e.max = max(ftr.exprs) + } + + if(is.null(names(ftr))) {names(ftr) = ""} + + pl = + ggplot(reduc, aes(x = DIM_1, y = DIM_2, color = EXPRS)) + if (.raster.scattermore == T) { + pl = pl + scattermore::geom_scattermore( + pointsize = .raster.scattermore.pointsize, pixels = .raster.scattermore.pixel + ) + } else { + if (pl.points == F) { + pl = pl + geom_point(shape = ".", alpha = 1) + } else { + pl = pl + geom_point(size = pt.size) + } + } + pl = pl + scale_color_gradientn( + colors = .colors, + na.value = "#DDDDDD", + limits = c(e.min, e.max), + breaks = pretty_breaks(4) + ) + pl = pl + guides( + color = guide_colorbar( + title = .leg.title, title.hjust = 0, barwidth = unit(legend.wh[1],'lines'), + barheight = unit(legend.wh[2], 'lines'), ticks.linewidth = 1.5/.pt + ) + ) + + {if(nchar(names(ftr)) == 0)ggtitle(ftr)} + + {if(!nchar(names(ftr)) == 0)ggtitle(names(ftr))} + + mytheme(base_size = base.size) + + theme( + aspect.ratio = 1, + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.spacing = unit(1, "lines"), + plot.title = element_text(hjust = 0.5, colour = "black", size = rel(.title.size)) + ) + + xlab(.x.title) + ylab(.y.title) + + if (.raster == T) { + pl = ggrastr::rasterize(pl, layers='Point', dpi=.raster.dpi) + } + + ftr.pl[[i]] = pl + } + + if (plot.grid == T) { + if(length(ftr.pl) <= 4) {ftr.pl = c(ftr.pl, vector(mode = "list", length = (4 - length(ftr.pl))))} + if(length(ftr.pl) > 4 && length(ftr.pl) <= 8) {ftr.pl = c(ftr.pl, vector(mode = "list", length = (8 - length(ftr.pl))))} + plot_grid(plotlist = ftr.pl, ncol = 4, scale = .95, align = "vh") + } else { + ftr.pl + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Dimension reduction: for phenodata (meta.data) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dimreduc_pheno = function( + .obj, + .target = NULL, + .reduc = "umap", + .raster.ggrastr = FALSE, + .raster.scattermore = FALSE, + .raster.scattermore.pixel = c(512,512), + .raster.scattermore.pointsize = 0, + .raster.dpi = 150, + .sort = F, + na_cutoff = NULL, + .col.palette = "2", + .col.pal.dicrete = ggthemes::tableau_color_pal()(10), + .col.scico = "roma", + .col.scico.d = -1, + quantile.fltr = F, + pl.points = F, + pt.size = 1 +) { + + reduc = data.frame(.obj@reductions[[.reduc]]@cell.embeddings) + colnames(reduc) = c("DIM_1", "DIM_2") + reduc = cbind(.obj@meta.data, reduc) + + if(is.null(.target)) { + reduc$tmp = "1" + .target = "tmp" + cont = F + } else { + cont = is.numeric(reduc[[.target]]) + } + + if(grepl("sne", .reduc, ignore.case = T)) { + .x.title = "tSNE 1"; .y.title = "tSNE 2" + } else { + .x.title = "DIM 1"; .y.title = "DIM 2" + } + if(grepl("umap", .reduc, ignore.case = T)) { + .x.title = "UMAP 1"; .y.title = "UMAP 2" + } else { + .x.title = "DIM 1"; .y.title = "DIM 2" + } + + if(quantile.fltr) { + qu.max = quantile(reduc[[.target]][ reduc[[.target]] > 0 ], .999) + reduc[[.target]][reduc[[.target]] > qu.max] = qu.max + } + + if(.sort == T) { + reduc = reduc[order(reduc[[.target]], decreasing = F), ] + } else { + set.seed(1234) + reduc = reduc %>% dplyr::sample_frac(1L, replace = FALSE) # permute rows randomly + } + + if(!is.null(na_cutoff)) { + e.min = min(reduc[[.target]][reduc[[.target]] > na_cutoff]) + e.max = max(reduc[[.target]]) + } else if (is.null(na_cutoff) & cont) { + e.min = min(reduc[[.target]]) + e.max = max(reduc[[.target]]) + } + + col.cont = list( + "1" = rev(MetBrewer::met.brewer("Hokusai1",n=100)), + "2" = scico::scico(30, palette = .col.scico, direction = .col.scico.d) + ) + + pl = + ggplot(data = reduc, aes(x = DIM_1, y = DIM_2, col = .data[[.target]])) + if (.raster.scattermore == T) { + pl = pl + scattermore::geom_scattermore( + pointsize = .raster.scattermore.pointsize, pixels = .raster.scattermore.pixel + ) + } else { + if (pl.points == F) { + pl = pl + geom_point(shape = ".", alpha = 1) + } else { + pl = pl + geom_point(size = pt.size) + } + } + pl = pl + theme( + aspect.ratio = 1, + panel.spacing = unit(1, "lines"), + legend.justification = c(0,.5), + legend.title = element_blank(), + plot.title = element_text(hjust = 0, face = "bold", colour = "black", size = rel(1)) + ) + + xlab(.x.title) + ylab(.y.title) + if(cont) { + pl = pl + + scale_color_gradientn( + colors = col.cont[[.col.palette]], + na.value = "#DDDDDD", + limits = c(e.min, e.max), + breaks = scales::pretty_breaks(4) + ) + + guides( + color = guide_colorbar( + title.hjust = 0, barwidth = unit(.4, 'lines'), barheight = unit(6, 'lines') + ) + ) + } else { + pl = pl + + scale_color_manual(values = .col.pal.dicrete, na.value = "#BBBBBB") + + guides(alpha = 'none') + + guides(colour = guide_legend(ncol = 1, override.aes = list(size=6, shape = 16, alpha = 1))) + + } + + if (.raster.ggrastr == T) { + ggrastr::rasterize(pl, layers='Point', dpi=.raster.dpi) + } else { + pl + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DimReduc colored by celltype +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dimreduc_celltypes = function( + obj = NULL, + dim = "wnnUMAP", + ct.to.pl = "celltype_short_3", + pt.size = .1, + leg.size = 2.5, + leg.ncol = 1, + col.cc = F, + base.size = 8, + ncol1 = 1, + ncol2 = 1, + ncol3 = 1, + raster = F, + raster.pt.size = 1 +) { + + pd = get_metadata(obj) + pd$DIM1 = pd[[paste0(dim, "_1")]] + pd$DIM2 = pd[[paste0(dim, "_2")]] + + if(col.cc == T) { + pd.cc = subset(pd, CellCycle == T) + pd.cc[[ct.to.pl]] = "Cycling" + pd = droplevels(pd[!pd$cell %in% pd.cc$cell, ]) + } + + minor.cts = names(table(pd[[ct.to.pl]])[table(pd[[ct.to.pl]]) < 10]) + pd.minor = pd[pd[[ct.to.pl]] %in% minor.cts, ] + pd = pd[!pd[[ct.to.pl]] %in% minor.cts, ] + pd = droplevels(pd) + + pd.lym = pd[grepl("T-Cell|B-Cell|NK|gdT|^Plasma", pd[[ct.to.pl]]), ] + pd.lym[[ct.to.pl]] = factor( + pd.lym[[ct.to.pl]], + levels = names(ct.col[names(ct.col) %in% pd.lym[[ct.to.pl]]]) + ) + + pd.my = pd[grepl("Mono|Macrophage|other DC|cDC|pDC|Erythrocyte|Platelet", pd[[ct.to.pl]]), ] + pd.my[[ct.to.pl]] = factor( + pd.my[[ct.to.pl]], + levels = names(ct.col[names(ct.col) %in% pd.my[[ct.to.pl]]]) + ) + + pd.other = pd[!pd[[ct.to.pl]] %in% as.character(c(unique(pd.lym[[ct.to.pl]]), unique(pd.my[[ct.to.pl]]))), ] + if(col.cc == T) { + pd.other = rbind(pd.other, pd.cc) + } + pd.other[[ct.to.pl]] = factor( + pd.other[[ct.to.pl]], + levels = names(ct.col[names(ct.col) %in% pd.other[[ct.to.pl]]]) + ) + + stopifnot( + length(colnames(obj)) == ( nrow(pd.lym) + nrow(pd.my) + nrow(pd.other) + nrow(pd.minor) ) + ) + + pl = ggplot() + if(raster == T) { + pl = pl + scattermore::geom_scattermore(data = pd.other, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + } else { + pl = pl + geom_point(data = pd.other, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + } + pl = pl + guides(colour = guide_legend( + title = "Other", title.position = "top", ncol = ncol1, order = 3, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(ct.col, setNames("#BBBBBB", "green")), na.value = "green") + + ggnewscale::new_scale_color() + if(raster == T) { + pl = pl + scattermore::geom_scattermore(data = pd.my, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + } else { + pl = pl + geom_point(data = pd.my, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + } + pl = pl + guides(colour = guide_legend( + title = "Myeloid", title.position = "top", ncol = ncol2, order = 2, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(ct.col, setNames("#BBBBBB", "Cycling")), na.value = "green") + + ggnewscale::new_scale_color() + if(raster == T) { + pl = pl + scattermore::geom_scattermore(data = pd.lym, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), pointsize = raster.pt.size) + } else { + pl = pl + geom_point(data = pd.lym, aes(x = DIM1, y = DIM2, color = .data[[ct.to.pl]]), shape = ".") + } + pl = pl + guides(colour = guide_legend( + title = "Lymphoid", title.position = "top", ncol = ncol3, order = 1, override.aes = list(shape = 16, size = leg.size) + )) + + scale_colour_manual(values = c(ct.col, setNames("#FFB92D", "Cycling")), na.value = "green") + + mytheme(base_size = base.size) + + theme( + legend.text = element_text(size = rel(.9)), + legend.spacing.y = unit(1, 'mm'), + legend.key.size = unit(3, "mm"), + legend.position = "right", + axis.text = element_blank(), + axis.ticks = element_blank(), + panel.border = element_blank(), + plot.title = element_text(size = 10, hjust = 1.1, face = "plain") + ) + + xlab("UMAP 1") + ylab("UMAP 2") + + pl +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Box plots for cell fractions per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +sc_ct_sample_fraction = function( + inpMeta, + label, + group.facet = "None", + group.color = "None", + group.color.pal = c("#6699CC", "#997700"), + dot.color = "None", + group.psz = .5, + base.size = 8, + scales = "fixed", + order.by.ave.prop = T, + nbr.cell.cut = 100, + filter.ct = NULL +){ + + library(ggh4x) + + inpMeta = data.frame(inpMeta) + + if(dot.color == "None"){ + dot.color = NULL + } + + cell.ident = inpMeta + cell.ident$celltype = cell.ident[[label]] + cell.ident = droplevels(cell.ident) + + cell.ident$groups_ct = paste0( + cell.ident[[group.facet]], "_xx_", cell.ident[[group.color]], "_yy_", cell.ident$celltype + ) + + # Extract total cell number per group + total_cells = data.frame(table(cell.ident$groups_ct)) %>% + dplyr::rename(groups_ct = Var1, groups_ct_number = Freq) + + total_cells$celltype = gsub(".+_yy_", "", total_cells$groups_ct) + total_cells$groups = gsub("_yy_.+", "", total_cells$groups_ct) + + l = list() + for (i in unique(total_cells$groups)) { + + target = as.character(total_cells[total_cells$groups %in% i, ]$groups_ct) + + # Calculate and extract percents of cells per cluster per group + g = cell.ident[cell.ident$groups_ct %in% target, ] + perc_per_groups_ct <- prop.table(x = table(g$groups_ct, g$orig.ident), margin = 2) * 100 + + # Remove sample not included in this group + empty_columns = colSums(is.na(perc_per_groups_ct) | perc_per_groups_ct == "") == nrow(perc_per_groups_ct) + perc_per_groups_ct = perc_per_groups_ct[, !empty_columns, drop = F] + + perc_per_groups_ct = data.frame(perc_per_groups_ct) %>% + dplyr::rename(groups_ct = Var1, orig.ident = Var2, perc_sample = Freq) + l[[i]] = perc_per_groups_ct + } + + perc_per_groups_ct = do.call("rbind", l) + rownames(perc_per_groups_ct) = NULL + + check = perc_per_groups_ct %>% + dplyr::group_by(orig.ident) %>% + dplyr::summarise(Frequency = sum(perc_sample)) %>% + data.frame() + stopifnot(!any(check$Frequency != "100")) + + perc_per_groups_ct = merge(perc_per_groups_ct, total_cells, by = c("groups_ct")) + + # Add groups for ggplot + if(!is.null(dot.color)) { + perc_per_groups_ct$dot.color = cell.ident[[dot.color]][match(perc_per_groups_ct$orig.ident, cell.ident$orig.ident)] + } else { + perc_per_groups_ct$dot.color = "None" + } + + perc_per_groups_ct$group_facet = gsub("_xx_.+", "", perc_per_groups_ct$groups) + perc_per_groups_ct$group_facet = factor( + perc_per_groups_ct$group_facet, levels = levels(cell.ident[[group.facet]]) + ) + + perc_per_groups_ct$group_color = gsub(".*_xx_", "", perc_per_groups_ct$groups) + perc_per_groups_ct$group_color = factor( + perc_per_groups_ct$group_color, levels = levels(cell.ident[[group.color]]) + ) + + # Remove NA + if(any(is.na(perc_per_groups_ct$group_color))) { + print("NA is present in the data") + perc_per_groups_ct = perc_per_groups_ct[!is.na(perc_per_groups_ct$group_color), ] + } + perc_per_groups_ct = droplevels(perc_per_groups_ct) + + # Remove celltypes with less than n cells in two groups + df.sum = perc_per_groups_ct %>% + group_by(celltype, groups, group_facet) %>% + dplyr::slice(1) + df.sum = df.sum %>% dplyr::group_by(celltype, group_facet) %>% + dplyr::summarise(n = sum(groups_ct_number)) %>% + dplyr::filter(n < nbr.cell.cut) %>% + data.frame() + if(nrow(df.sum) != 0) { + df.sum$REMOVE = paste0(df.sum$celltype, "_", df.sum$group_facet) + } else { + df.sum$REMOVE = NULL + } + perc_per_groups_ct$TMP = paste0(perc_per_groups_ct$celltype, "_", perc_per_groups_ct$group_facet) + perc_per_groups_ct = perc_per_groups_ct[!perc_per_groups_ct$TMP %in% unique(df.sum$REMOVE), ] + + # Remove celltypes, where only one sample hast cells + perc_per_groups_ct$TMP = perc_per_groups_ct$perc_sample > 0 + df.sum = perc_per_groups_ct %>% dplyr::group_by(celltype, group_facet) %>% + dplyr::summarise(n = sum(TMP)) %>% + dplyr::filter(n == 1) %>% + data.frame() + if(nrow(df.sum) != 0) { + df.sum$REMOVE = paste0(df.sum$celltype, "_", df.sum$group_facet) + } else { + df.sum$REMOVE = NULL + } + perc_per_groups_ct$TMP = paste0(perc_per_groups_ct$celltype, "_", perc_per_groups_ct$group_facet) + perc_per_groups_ct = perc_per_groups_ct[!perc_per_groups_ct$TMP %in% unique(df.sum$REMOVE), ] + perc_per_groups_ct$TMP = NULL + + perc_per_groups_ct$perc_sample = perc_per_groups_ct$perc_sample / 100 + + if(!is.null(filter.ct)) { + perc_per_groups_ct = perc_per_groups_ct[grepl(filter.ct, perc_per_groups_ct$celltype), ] + perc_per_groups_ct = droplevels(perc_per_groups_ct) + } + + if(order.by.ave.prop == T){ + lvls = perc_per_groups_ct %>% + dplyr::group_by(celltype, group_facet) %>% + dplyr::summarise(mean_prop = mean(perc_sample)) %>% + dplyr::arrange(-mean_prop) %>% dplyr::select(celltype) %>% unlist + } else { + lvls = naturalsort(unique(as.character(perc_per_groups_ct$celltype))) + } + perc_per_groups_ct$celltype = factor(perc_per_groups_ct$celltype, levels = unique(lvls)) + + set.seed(123) + pl = ggplot(perc_per_groups_ct, aes(celltype, perc_sample)) + pl = pl + geom_boxplot( + aes(fill = group_color), position = position_dodge2(.85, padding = .2, preserve = "single"), + outlier.shape = NA, fatten = 1.5, linewidth = .2 + ) + if(!is.null(dot.color)) { + pl = pl + geom_point( + aes(col = dot.color, group = group_color), size=group.psz, + position = position_jitterdodge(jitter.width = .2) + ) + scale_color_manual(values = colors_use.10) + pl = pl + guides(color = guide_legend(title = dot.color, nrow = 1, override.aes = list(size = 2.5))) + } else { + pl = pl + geom_point( + aes(col = dot.color, group = group_color), size=group.psz, + position = position_jitterdodge(jitter.width = .2) + ) + + scale_color_manual(values = c("#555555")) + + guides(color = "none") + } + pl = pl + ylab("Cell type proportion per sample") + + xlab(NULL) + + labs(fill = group.color) + + mytheme_grid(base_size = base.size) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor.x = element_blank(), + panel.grid.minor.y = element_blank(), + legend.position = "bottom", + axis.title.x = element_blank(), + axis.text.x = element_text(angle=45, vjust=1, hjust=1), + panel.spacing = unit(1, "lines"), + strip.text = element_text(size = rel(1), face = "plain"), + strip.background = element_blank(), + ggh4x.facet.nestline = element_line(colour = "#1A242F") + ) + + scale_x_discrete(drop=FALSE) + + scale_fill_manual(values = group.color.pal, drop = F, na.value = "#BBBBBB") + + # Color + pl = pl + guides(fill = guide_legend(nrow = 1)) + + # Facetting + if(group.facet != "None") { + pl = + pl + facet_wrap2(~ group_facet, ncol = 1, drop = F, scales = scales) + + annotate("segment",x=Inf,xend=-Inf,y=Inf,yend=Inf,color="black",lwd=.5) + } + + return(pl) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Celltype composition per sample +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +comp_celltypes = function( + p.data = NULL, + t = "celltype_short_2", + group.facet = "None", + group.color = "RESPONSE_CONSENSUS", + dot.color = "PRODUCT", + base.size = 8, + title.size = 10, + group.psz = .35, + nbr.cell.cut = 50 +) { + + p.data = subset(p.data, CAR_BY_EXPRS == F) + + comp.pl = + sc_ct_sample_fraction( + inpMeta = p.data, + label = t, + group.facet, + group.color = group.color, + dot.color = dot.color, + nbr.cell.cut = nbr.cell.cut, + group.psz = group.psz, + base.size = base.size + ) + + scale_y_continuous( + trans = scales::pseudo_log_trans(sigma = 0.0001, base = 10), + breaks=c(0, 0.001, 0.01, 0.1, 1) + ) + + coord_cartesian(ylim=c(0, 2)) + + # Speckle + res.speckle = speckle::propeller( + clusters = p.data[[t]], sample = p.data$orig.ident, + group = p.data$RESPONSE_CONSENSUS, transform = "asin" + ) + + dat = dplyr::distinct(comp.pl$data, celltype) + res.speckle = res.speckle[rownames(res.speckle) %in% dat$celltype, ] + res.speckle = res.speckle[dat$celltype, ] + res.speckle$yloc = max(comp.pl$data$perc_sample) + .75 + res.speckle = add_signif(res.speckle,"P.Value", "pval_star", pval.relax = T) + colnames(res.speckle)[1] = "celltype" + res.speckle = droplevels(res.speckle) + + comp.pl + + geom_text(data = res.speckle, aes(y = yloc, label = pval_star), size = 2, position = position_dodge(width = .75)) + + guides(fill = guide_legend(title = NULL, order = 1)) + + guides(color = guide_legend(title = NULL, override.aes = list(shape = 16, size = 2.5))) + + theme( + plot.title = element_text(size = title.size, hjust = 0.5, face = "plain"), + legend.position = "right" + ) + + scale_color_manual(values = c("#994455", "black")) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA scatterplot with log fold change (y-axis) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dgea_plot = function( + dgea.res = NULL, + dgea.res.sign = NULL, + nbr.tops = 5, + base.size = 8, + text.repel.size = 2.5, + text.de.nbr.size = 2.5, + text.cl.size = 2.5, + ylim.extend.up = .5, + ylim.extend.dn = .7, + box.padding = 0.25 +){ + + dgea.res$ID = paste0(dgea.res$cluster, "_", dgea.res$feature) + dgea.res.sign$ID = paste0(dgea.res.sign$cluster, "_", dgea.res.sign$feature) + # dgea.res.sign = dgea.res[dgea.res$padj < fdr & abs(dgea.res$logFC) > log(fc), ] + dgea.res = dgea.res[dgea.res$cluster %in% unique(dgea.res.sign$cluster), ] + + cluster.de.summary = dgea.res.sign %>% + dplyr::mutate(IsUp = logFC > 0) %>% + dplyr::group_by(cluster) %>% + dplyr::summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>% + dplyr::mutate(Down = -Down) %>% + tidyr::gather(key = "Direction", value = "Count", -cluster) + + cluster.de.summary = cluster.de.summary[order(cluster.de.summary$cluster), ] + cluster.de.summary$cluster = factor(cluster.de.summary$cluster, levels = unique(cluster.de.summary$cluster)) + + cl.label = paste0( + "Up: ", + subset(cluster.de.summary, Direction == "Up")$Count, + "\nDown: ", + abs(subset(cluster.de.summary, Direction == "Down")$Count) + ) + + top5_up = dgea.res.sign %>% + dplyr::filter(logFC > 0) %>% + dplyr::group_by(cluster) %>% + dplyr::top_n(n = nbr.tops, wt = logFC) + top5_up$GENE_CL = paste0(top5_up$feature, "_", top5_up$cluster) + + top5_down = dgea.res.sign %>% + dplyr::filter(logFC < 0) %>% + dplyr::group_by(cluster) %>% + dplyr::top_n(n = -nbr.tops, wt = logFC) + top5_down$GENE_CL = paste0(top5_down$feature, "_", top5_down$cluster) + + dgea.res$GENE_CL = paste0(dgea.res$feature, "_", dgea.res$cluster) + dgea.res = dgea.res %>% mutate(label_up = ifelse(GENE_CL %in% top5_up$GENE_CL, feature, "")) + dgea.res = dgea.res %>% mutate(label_down = ifelse(GENE_CL %in% top5_down$GENE_CL, feature, "")) + dgea.res$SIGNIFICANT = dgea.res$ID %in% dgea.res.sign$ID + dgea.res$SIGNIFICANT = ifelse(dgea.res$SIGNIFICANT == T, "yes", "no") + + axis.max = max(abs(dgea.res$logFC)) + .75 + no.cl = length(unique(dgea.res$cluster)) + top1.up = dgea.res.sign %>% group_by(cluster) %>% + top_n(n = 1, wt = logFC) %>% + data.frame() + rownames(top1.up) = top1.up$cluster + top1.up = top1.up[levels(cluster.de.summary$cluster), ] + top1.down = dgea.res.sign %>% group_by(cluster) %>% + top_n(n = -1, wt = logFC) %>% + data.frame() + rownames(top1.down) = top1.down$cluster + top1.down = top1.down[levels(cluster.de.summary$cluster), ] + + stopifnot(identical(top1.up$cluster, top1.down$cluster)) + + pos = position_jitter(width = 0.35, seed = 1234) + + data = data.frame( + xmin = seq(.6, no.cl), + xmax = seq(.2, no.cl) + 1.2, + ymin = top1.down$logFC - .1, + ymax = top1.up$logFC + .1, + group = top1.up$cluster + ) + + set.seed(1234) + # dge.pl = + ggplot() + + ylim(c(-axis.max, axis.max)) + + geom_hline(yintercept = 0, lwd = .3) + + geom_jitter( + data = subset(dgea.res, SIGNIFICANT == "no" & abs(logFC) > .15), + aes(x= cluster, y= logFC, color = SIGNIFICANT), position = pos, size = .3 + ) + + geom_jitter( + data = subset(dgea.res, SIGNIFICANT == "yes"), + aes(x= cluster, y= logFC, color = SIGNIFICANT), position = pos, size = .3 + ) + + geom_rect( + data = data, + aes(x = group, xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax), fill = "#DDDDDD", alpha = .3 + ) + + geom_rect( + data = data.frame(xmin = seq(0.5,no.cl), xmax = seq(0.5,no.cl) + 1, ymin = -.15, ymax = .15), + aes(xmin = xmin, xmax = xmax, ymin = ymin, ymax = ymax), fill = "white", color = "#BBBBBB", lwd = .2 + ) + + geom_text_repel( + data = subset(dgea.res, SIGNIFICANT == "yes"), + mapping = aes(x = cluster, logFC, label = label_up), + position = pos, + size = text.repel.size, + max.overlaps = 100, + min.segment.length = 0, + box.padding = box.padding, + segment.size = .1, + direction = "y", + ylim = c(.1, max(dgea.res$logFC) + ylim.extend.up) + ) + + geom_text_repel( + data = subset(dgea.res, SIGNIFICANT == "yes"), + mapping = aes(x = cluster, logFC, label = label_down), + position = pos, + size = text.repel.size, + max.overlaps = 100, + min.segment.length = 0, + box.padding = box.padding, + segment.size = .1, + direction = "y", + ylim = c(-.1, min(dgea.res$logFC) - ylim.extend.dn) + ) + + mytheme(base_size = base.size) + + theme( + legend.position="bottom", + axis.line.y = element_line(colour = "black"), + panel.border = element_blank(), + axis.text.x = element_blank(), + axis.ticks.x = element_blank(), + legend.margin = margin(t=-15), + # legend.title = element_text(margin = margin(r = 10)), + legend.spacing.x = unit(.1, 'mm') + ) + + scale_color_manual(values = c("yes" = "#6699CC", "no" = "#BBBBBB")) + + labs(x = NULL, y = "Average log fold change", colour = paste0("FDR <0.05 and (FC >1.5 or FC <1.5)")) + + annotate("text", x = 1:(no.cl), y = 0, label = data$group, color = "black", size = text.de.nbr.size) + + annotate("text", x = 1:(no.cl), y = max(abs(dgea.res$logFC) + .75), label = cl.label, colour = "#6699CC", size = text.cl.size) + + guides(colour = guide_legend(override.aes = list(size=2))) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# DGEA Volcano +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +dgea_volcano = function( + dgea.res = NULL, + dgea.res.sign = NULL, + nbr.tops = 7, + cl.label = NULL, + sort.by = "logFC", + facet.scales = "free", + nudge_x = 2, + set.xlim = F, + axis.max.ext = 0, + box.padding = 0.3, + label.padding = .12 +){ + + dgea.res$ID = paste0(dgea.res$cluster, "_", dgea.res$feature) + dgea.res.sign$ID = paste0(dgea.res.sign$cluster, "_", dgea.res.sign$feature) + dgea.res = dgea.res[dgea.res$cluster %in% unique(dgea.res.sign$cluster), ] + + # cluster.de.summary = dgea.res.sign %>% + # dplyr::mutate(IsUp = logFC > 0) %>% + # dplyr::group_by(cluster) %>% + # dplyr::summarise(Up = sum(IsUp), Down = sum(!IsUp)) %>% + # dplyr::mutate(Down = -Down) %>% + # tidyr::gather(key = "Direction", value = "Count", -cluster) + # + # cluster.de.summary = cluster.de.summary[order(cluster.de.summary$cluster), ] + # cluster.de.summary$cluster = factor(cluster.de.summary$cluster, levels = unique(cluster.de.summary$cluster)) + # + # cl.label = paste0( + # "Up: ", + # subset(cluster.de.summary, Direction == "Up")$Count, + # ", Down: ", + # abs(subset(cluster.de.summary, Direction == "Down")$Count) + # ) + # cl.label = setNames( + # paste0(as.character(unique(cluster.de.summary$cluster)), "\n", cl.label), + # as.character(unique(cluster.de.summary$cluster)) + # ) + + if(sort.by != "logFC") { + tops_up = dgea.res.sign %>% + dplyr::filter(logFC > 0) %>% + dplyr::group_by(cluster) %>% + dplyr::arrange(.data[[sort.by]], -abs(logFC)) %>% + dplyr::slice_head(n=nbr.tops) + tops_up$GENE_CL = paste0(tops_up$feature, "_", tops_up$cluster) + + tops_down = dgea.res.sign %>% + dplyr::filter(logFC < 0) %>% + dplyr::group_by(cluster) %>% + dplyr::arrange(.data[[sort.by]], -abs(logFC)) %>% + dplyr::slice_head(n=nbr.tops) + tops_down$GENE_CL = paste0(tops_down$feature, "_", tops_down$cluster) + } else { + tops_up = dgea.res.sign %>% + dplyr::filter(logFC > 0) %>% + dplyr::group_by(cluster) %>% + dplyr::top_n(n = nbr.tops, wt = .data[[sort.by]]) + tops_up$GENE_CL = paste0(tops_up$feature, "_", tops_up$cluster) + + tops_down = dgea.res.sign %>% + dplyr::filter(logFC < 0) %>% + dplyr::group_by(cluster) %>% + dplyr::top_n(n = nbr.tops, wt = -.data[[sort.by]]) + tops_down$GENE_CL = paste0(tops_down$feature, "_", tops_down$cluster) + } + + dgea.res$GENE_CL = paste0(dgea.res$feature, "_", dgea.res$cluster) + dgea.res = dgea.res %>% mutate(label_up = ifelse(GENE_CL %in% tops_up$GENE_CL, feature, "")) + dgea.res = dgea.res %>% mutate(label_down = ifelse(GENE_CL %in% tops_down$GENE_CL, feature, "")) + dgea.res$SIGNIFICANT = dgea.res$ID %in% dgea.res.sign$ID + dgea.res$SIGNIFICANT = factor(dgea.res$SIGNIFICANT, levels = c(TRUE, FALSE)) + + axis.max = max(abs(dgea.res$logFC)) + dgea.res = dgea.res %>% + mutate(padj = ifelse(padj == 0, min( padj[padj != min(padj)]), padj)) + + if(is.null(cl.label)) { + cl.label = setNames(unique(dgea.res$cluster), unique(dgea.res$cluster)) + } + + set.seed(42) + pl = + ggplot(dgea.res, aes(x = logFC, y = -log10(padj))) + + # geom_point(data = subset(dgea.res, SIGNIFICANT == F), aes(color = SIGNIFICANT), size = 1) + + # geom_point(data = subset(dgea.res, SIGNIFICANT == T), aes(color = SIGNIFICANT), size = 1) + + scattermore::geom_scattermore(data = subset(dgea.res, SIGNIFICANT == F), aes(color = SIGNIFICANT), pointsize = 3) + + scattermore::geom_scattermore(data = subset(dgea.res, SIGNIFICANT == T), aes(color = SIGNIFICANT), pointsize = 3) + + labs(x = "log fold change", y = "-log10(FDR)") + + theme( + panel.spacing = unit(1, "lines"), + panel.grid.minor = element_blank(), + legend.position="bottom", + strip.text = element_text(size = rel(1), face = "bold") + ) + + facet_wrap(~ cluster, scales = facet.scales, labeller = labeller(cluster = cl.label)) + + geom_label_repel( + data = subset(dgea.res, SIGNIFICANT == T & label_up != ""), + label = subset(dgea.res, SIGNIFICANT == T & label_up != "")$label_up, + segment.colour = "black", + size = 2.5, + direction = "y", + hjust = .5, + nudge_x = nudge_x, + nudge_y = -2, + segment.size = .2, + box.padding = box.padding, + label.padding = label.padding, + min.segment.length = 0, + max.overlaps = 50 + ) + + geom_label_repel( + data = subset(dgea.res, SIGNIFICANT == T & label_down != ""), + label = subset(dgea.res, SIGNIFICANT == T & label_down != "")$label_down, + segment.colour = "black", + size = 2.5, + direction = "y", + hjust = .5, + # xlim = c(NA, 0), + nudge_x = -nudge_x, + segment.size = .2, + box.padding = box.padding, + label.padding = label.padding, + min.segment.length = 0, + max.overlaps = 50 + ) + + scale_color_manual(values = c("TRUE" = "#6699CC", "FALSE" = "#BBBBBB")) + + labs(y = "-log10(FDR)", x = "Log fold change", colour = paste0("FDR <0.05 & abs(Fold-change) >1.5")) + + guides(colour = guide_legend(override.aes = list(size=3))) + if(set.xlim == T) { + pl = pl + xlim(-axis.max - axis.max.ext, axis.max + axis.max.ext) + } + pl +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Doptplot for ClusterProfiler compareCluster() +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ora_dotplot_cc = function( + ora.res = NULL, + genelist = NULL, + subset_cluster = NULL, + order.by.p = T, + nbr.tops = 5, + gg.title = NULL, + term.length = 50, + base.size = 8, + min.size = 3, + quantile.cut = F, + max.value = NULL, + x.text.size = rel(1), + facet = F, + facet.order = c(2, 3), + facet.lvls = NULL, + dot.range = c(1.5, 4.5) +) { + + res = ora.res@compareClusterResult + + if(!is.null(subset_cluster)) { + res = res[grepl(subset_cluster, res$Cluster), ] + res = droplevels(res) + } + + res = mutate(res, richFactor = Count / as.numeric(sub("/\\d+", "", BgRatio))) + res = res[res$Count >= min.size, ] + res$ID2 = paste0(res$Cluster, "_", res$ID) + + res.df = res + res = split(res, res$Cluster) + + tops.l = lapply(res, function(x){ + + if(order.by.p == T) { + x = x[order(x$p.adjust, decreasing = F), ] + } else { + x = x[order(x$richFactor, decreasing = T), ] + } + x = head(x, nbr.tops) + x + }) + + tops = as.data.frame(data.table::rbindlist(tops.l)) + res.df = subset(res.df, ID %in% tops$ID) + # res.df = subset(res.df, ID2 %in% tops$ID2) + + lvls = tops.l[[1]]$Description + lvls = c(lvls, setdiff(res.df$Description, lvls)) + res.df$Description = factor(res.df$Description, levels = rev(lvls)) + res.df$Cluster = factor(res.df$Cluster, levels = unique((res.df$Cluster))) + + res.df$zscore = NA + for (i in 1:nrow(res.df)) { + ct = as.character(res.df[i, ]$Cluster) + ftrs = unlist(strsplit(res.df[i, ]$geneID, "/")) + ftrs.lfc = names(genelist[[ct]][(genelist[[ct]]) %in% ftrs]) + gs.ftrs = as.numeric(gsub("/.*", "", res.df[i, ]$BgRatio)) + zscore = (length(ftrs.lfc[ftrs.lfc > 0]) - length(ftrs.lfc[ftrs.lfc < 0])) / sqrt(gs.ftrs) + res.df[i, ]$zscore = zscore + } + + if (quantile.cut == T) { + qu.p = quantile(res.df$zscore[res.df$zscore > 0], .97) + qu.n = quantile(res.df$zscore[res.df$zscore < 0], .03) + q.max = which.max(c(abs(qu.p), abs(qu.n))) + if(q.max == 1) { + res.df$zscore[res.df$zscore > qu.p] = qu.p + } else { + res.df$zscore[res.df$zscore < qu.n] = qu.n + } + } + + if(is.null(max.value)){ + max.value = max(abs(res.df$zscore)) + } else { + res.df$zscore[res.df$zscore > max.value] = max.value + res.df$zscore[res.df$zscore < -max.value] = -max.value + } + + default_labeller <- function(n) { + function(str){ + str <- gsub("_", " ", str) + yulab.utils::str_wrap(str, n) + } + } + + if(facet == T) { + res.df$xlabels = res.df[, facet.order[1]] + res.df$facet = res.df[, facet.order[2]] + } else { + res.df$xlabels = res.df$Cluster + } + + if(!is.null(facet.lvls)){ + res.df$facet = factor(res.df$facet, levels = facet.lvls) + } + + pl = + ggplot(res.df, aes(x = xlabels, y = Description, size = -log10(p.adjust), fill = zscore)) + + geom_point(colour="black", pch=21, stroke = .3) + + scale_size(range = dot.range) + + scale_fill_gradientn( + colours = cont.col, + limits = c(-max.value, max.value + ), breaks = pretty_breaks(n = 3), + ) + + mytheme_grid(base_size = base.size) + + theme( + panel.grid.minor = element_blank(), + panel.grid.major.x = element_blank(), + axis.text.x = element_text(angle=45, vjust=1, hjust=1, size = x.text.size), + legend.position = "right" + ) + + guides( + fill = guide_colorbar( + title = "Z-score", barwidth = unit(.4, 'lines'), + barheight = unit(5, 'lines'), order = 1, ticks.linewidth = 1.5/.pt + ), + size = guide_legend(title = "-Log10(FDR)", order = 2) + ) + + scale_y_discrete(labels = default_labeller(term.length)) + + xlab(NULL) + ylab(NULL) + + ggtitle(gg.title) + + if(facet == T) { + pl = pl + facet_grid(~ facet, scales = "free_x", space = "free") + # pl = pl + facet_wrap(~ facet, scales = "free", nrow = 2) + } + pl +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Ligand-Receptor analysis +# DB from LIANA. Parsed with iTALK +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ligand_receptor_analysis = function( + dgea.res.sign = NULL, + same.direction = F, + self.interaction = F, + font.size = 8 +) { + + if (any(!"iTALK" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + devtools::install_github("Coolgenome/iTALK", build_vignettes = TRUE) + } + library(iTALK) + + if (any(!"liana" %in% installed.packages())) { + # Sys.unsetenv("GITHUB_PAT") + remotes::install_github('saezlab/liana') + } + library(liana) + + consensus_omni <- liana::select_resource("Consensus")[[1]] + + liana.db = consensus_omni %>% + dplyr::select( + Ligand.ApprovedSymbol = source_genesymbol, + Receptor.ApprovedSymbol = target_genesymbol, + Classification = category_intercell_source + ) %>% + data.frame() + + getLRI = function( + res_post_splitted, + comm_list = c('growth factor','other','cytokine','checkpoint'), + custom.db = NULL + ){ + + comparison_grid = expand.grid( + seq_along(res_post_splitted), seq_along(res_post_splitted) + ) + res_post = NULL + for (i in seq(nrow(comparison_grid))){ + for(comm_type in comm_list){ + res_cat = FindLR( + res_post_splitted[[comparison_grid$Var1[i]]], + res_post_splitted[[comparison_grid$Var2[i]]], + datatype='DEG',comm_type=comm_type, database = custom.db + ) + + res_post<-rbind(res_post,res_cat) + } + } + return(res_post) + } + + if (same.direction == T) { + res_up_splitted = dgea.res.sign %>% + select(gene = feature, cell_type = cluster, logFC, p.value = pval, q.value = padj) %>% + filter(logFC > 0) + res_up_splitted = split(res_up_splitted, res_up_splitted$cell_type) + + res_dn_splitted = dgea.res.sign %>% + select(gene = feature, cell_type = cluster, logFC, p.value = pval, q.value = padj) %>% + filter(logFC < 0) + res_dn_splitted = split(res_dn_splitted, res_dn_splitted$cell_type) + + # pre_up_LRI = getLRI(res_up_splitted) + # pre_dn_LRI = getLRI(res_dn_splitted) + pre_dn_LRI = getLRI(res_dn_splitted, custom.db = liana.db, comm_list = unique(liana.db$Classification)) + pre_up_LRI = getLRI(res_up_splitted, custom.db = liana.db, comm_list = unique(liana.db$Classification)) + pre_LRI = bind_rows(pre_up_LRI, pre_dn_LRI) %>% distinct + } else { + res_splitted = dgea.res.sign %>% + select(gene = feature, cell_type = cluster, logFC, p.value = pval, q.value = padj) + res_splitted = split(res_splitted, res_splitted$cell_type) + pre_LRI = getLRI(res_splitted, custom.db = liana.db, comm_list = unique(liana.db$Classification)) + } + + lr.df = pre_LRI + + lr.df$LIGAND_ID = paste0(lr.df$ligand, "_", lr.df$cell_from) + lr.df$RECEPTOR_ID = paste0(lr.df$receptor, "_", lr.df$cell_to) + lr.df$LIGAND_AVE_EXPRS = dgea.res.sign$avgExpr[match(lr.df$LIGAND_ID, rownames(dgea.res.sign))] + lr.df$RECEPTOR_AVE_EXPRS = dgea.res.sign$avgExpr[match(lr.df$RECEPTOR_ID, rownames(dgea.res.sign))] + lr.df$AVE_EXPRS = rowMeans(cbind(abs(lr.df$LIGAND_AVE_EXPRS), abs(lr.df$RECEPTOR_AVE_EXPRS))) + lr.df$AVE_LFC = rowMeans(cbind(abs(lr.df$cell_from_logFC), abs(lr.df$cell_to_logFC))) + + lr.df$LR_PAIR = paste0(lr.df$ligand, " -> ", lr.df$receptor) + lr.df = lr.df %>% mutate( + LR_DIR = case_when( + cell_from_logFC > 0 & cell_to_logFC > 0 ~ "L:up | R:up", + cell_from_logFC < 0 & cell_to_logFC < 0 ~ "L:dn | R:dn", + cell_from_logFC > 0 & cell_to_logFC < 0 ~ "L:up | R:dn", + cell_from_logFC < 0 & cell_to_logFC > 0 ~ "L:dn | R:up" + ) + ) + lr.df$LR_DIR = factor(lr.df$LR_DIR, levels = c("L:up | R:up", "L:dn | R:dn", "L:up | R:dn", "L:dn | R:up")) + + if(self.interaction == F){ + lr.df = lr.df[!lr.df$cell_from == lr.df$cell_to, ] + } + + max.lfc = max(c(abs(lr.df$cell_from_logFC), abs(lr.df$cell_to_logFC))) + ggplot(lr.df, aes(x = cell_to, y = LR_PAIR, fill = LR_DIR, size = AVE_LFC)) + + geom_point(colour="black", pch=21, stroke = .3) + + scale_size(range = c(1, 4)) + + # facet_wrap( ~ cell_from, nrow = 1, scales = "free_x") + + facet_grid( ~ cell_from, scales = "free_x", space = "free_x") + + guides(color = guide_legend( + title = "LFC Direction", ncol = 1, order = 2, override.aes = list(shape = 16, size = 3.5) + )) + + scale_fill_manual(values = c( + "L:up | R:up" = "#BB5566", + "L:dn | R:dn" = "#4477AA", + "L:up | R:dn" = "#555555", + "L:dn | R:up" = "#BBBBBB" + )) + + mytheme_grid(base_size = font.size) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor.x = element_blank(), + panel.grid.minor.y = element_blank(), + legend.position = "bottom", + plot.title = element_text(hjust = 0.5, face = "plain", colour = "black", size = rel(1.25)), + axis.title.x = element_text(hjust = 0.5, face = "plain", colour = "black", size = rel(1.25)), + axis.text.x = element_text(angle=45, vjust=1, hjust=1) + ) + + xlab("Target") + ylab("Ligand -> Receptor") + + labs(size = "Mean LFC") + + ggtitle("Source") + + + # ftrs.col = structure( + # c(rep('#BBBBBB',length(pre_LRI$ligand)), rep('#000000',length(pre_LRI$receptor))), + # names = c(pre_LRI$ligand, pre_LRI$receptor) + # ) + # + # options(repr.plot.width=7, repr.plot.height=7) + # # circos.clear() + # LRPlot2( + # data = pre_LRI, + # datatype='DEG', + # cell_col = ct.col, + # gene_col = ftrs.col, + # track.height_1 = circlize::uh(2.5,'mm'), + # link.arr.lwd = pre_LRI$cell_from_logFC, + # gene_size = .7, + # celltype_size = 1 + # ) + # + # p1_recorded <- recordPlot() + # ggdraw(p1_recorded) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Bubble plot for cell identity markers +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +mrkr_bubble = function( + dgea.res = NULL, + se.obj = NULL, + group = "celltype", + effect.size = "logFC", + positive.effect.size = T, + order.by.effect.size = T, + nbr.tops = 5, + quantile.cut = .99, + features = NULL, + font.size = 10, + aspectRatio = 2, + pt.size = 1, + rm.rp = T, + export.table = F +) { + + library(scico) + library(circlize) + library(ComplexHeatmap) + + DefaultAssay(se.obj) = "RNA" + + dgea.res.all = dgea.res + dgea.res = dgea.res[dgea.res$significant == T, ] + + if (rm.rp == T) { + dgea.res = dgea.res[!grepl('^RP', dgea.res$feature), ] + } + + if(is.null(features)) { + + dge.res = dgea.res[!is.na(dgea.res[[effect.size]]), ] + if (positive.effect.size == T) { + dge.res = dge.res[dge.res[[effect.size]] > 0, ] + } + + if (order.by.effect.size == T) { + dge.res = dge.res %>% arrange(!!as.name(group), desc(abs(!!as.name(effect.size)))) + } else { + dge.res = dge.res %>% dplyr::arrange(!!as.name(group), padj) + } + + tops = dge.res %>% + group_by(!!as.name(group)) %>% + slice_head(n = nbr.tops) %>% data.frame() + export.tops = tops + tops = tops$feature + + ct.keep = unique(as.character(dge.res[[group]])) + se.obj = se.obj[, se.obj@meta.data[[group]] %in% ct.keep] + se.obj@meta.data = droplevels(se.obj@meta.data) + + } else { + tops = features + } + + se.obj = se.obj[unique(tops), ] + mat = AverageExpression(se.obj, group.by = group, assays = "RNA", slot = "data")[[1]] + mat = t(scale(t(mat))) + df = reshape2::melt(mat) + colnames(df) = c("GENE", "CT", "AVE") + + v.max = quantile(df$AVE, quantile.cut) + df$AVE[abs(df$AVE) > v.max] = v.max + + df$PERC = dgea.res.all$pct_in[match(paste0(df$GENE, "_", df$CT), paste0(dgea.res.all$feature, "_", dgea.res.all$celltype))] + df$CT = gsub("\\.", " ", df$CT) + + pl = + ggplot(df, aes(x = CT,y = GENE, size = PERC)) + + geom_point(aes(color = AVE)) + + scale_size(range = c(0.5, pt.size)) + + scale_color_scico(palette = "vik", midpoint = 0, limits = c(-v.max,v.max)) + + mytheme(base_size = font.size) + + theme( + panel.grid.major.x = element_blank(), + panel.grid.minor = element_blank(), + panel.spacing = unit(.5, "lines"), + axis.text.x = element_text(angle=45, hjust=1, vjust = 1), + axis.text.y = element_text(size = rel(1)), + axis.title.x = element_blank(), + axis.title.y = element_blank(), + axis.ticks.x = element_blank(), + aspect.ratio = aspectRatio + ) + + guides( + size = guide_legend(title = "Percent\nExpressed"), + color = guide_colorbar( + title = "Z-scored\nAverage\nExpression", order = 1, + title.hjust = 0, barwidth = unit(.5, 'lines'), barheight = unit(5, 'lines') + ) + ) + + if(export.table == T) { + export.tops + } else { + pl + } +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Alluvial for clones +# Modified from: https://github.com/ncborcherding/scRepertoire/tree/master +# Modification: for y-axis: proportion or frequency +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +compareClonotypes.cust <- function( + df, + cloneCall = "strict", + chain = "both", + samples = NULL, + clonotypes = NULL, + numbers = NULL, + split.by = NULL, + graph = "alluvial", + exportTable = FALSE, + scale = T +){ + df <- scRepertoire:::list.input.return(df, split.by) + cloneCall <- scRepertoire:::theCall(cloneCall) + df <- scRepertoire:::checkBlanks(df, cloneCall) + if (!is.null(numbers) & !is.null(clonotypes)) { + stop("Make sure your inputs are either numbers or clonotype sequences.") + } + Con.df <- NULL + for (i in seq_along(df)) { + if (chain != "both") { + df[[i]] <- off.the.chain(df[[i]], chain, cloneCall) + } + tbl <- as.data.frame(table(df[[i]][,cloneCall])) + + tbl$Frequency = tbl$Freq + tbl[,2] <- tbl[,2]/sum(tbl[,2]) + colnames(tbl) <- c("Clonotypes", "Proportion", "Frequency") + tbl$Sample <- names(df[i]) + Con.df <- rbind.data.frame(Con.df, tbl) + } + if (!is.null(samples)) { + Con.df <- Con.df[Con.df$Sample %in% samples,] } + if (!is.null(clonotypes)) { + Con.df <- Con.df[Con.df$Clonotypes %in% clonotypes,] } + if (!is.null(numbers)) { + top <- Con.df %>% + group_by(Con.df[, 4]) %>% + slice_max(n = numbers, order_by = Proportion, with_ties = FALSE) + Con.df <- Con.df[Con.df$Clonotypes %in% top$Clonotypes,] } + if (nrow(Con.df) < length(unique(Con.df$Sample))) { + stop("Reasses the filtering strategies here, there is not + enough clonotypes to examine.") } + if (exportTable == TRUE) { return(Con.df)} + + if(scale == T) { + Con.df$y_axis = Con.df$Proportion + y.title = "Proportion" + } else { + Con.df$y_axis = Con.df$Frequency + y.title = "Cells" + } + + plot <- ggplot(Con.df, aes(x = Sample, fill = Clonotypes, group = Clonotypes, + stratum = Clonotypes, alluvium = Clonotypes, + y = y_axis, label = Clonotypes)) + + theme_classic() + + theme(axis.title.x = element_blank()) + + ylab(y.title) + + if (graph == "alluvial") { + plot <- plot + geom_stratum() + geom_flow(stat = "alluvium") + } else if (graph == "area") { + plot <- plot + + geom_area(aes(group = Clonotypes), color = "black") } + return(plot) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Violin plots for adt genes. Figure 8 A-C +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +plot_marker = function( + obj = NULL, + obj.assay = "ADT", + ftrs = NULL, + col = NULL, + col.lvl = NULL, + label = NULL +){ + + obj@meta.data = droplevels(obj@meta.data) + + DefaultAssay(obj) = obj.assay + expr.data = FetchData(obj, ftrs, slot = "data") + stopifnot(identical(rownames(expr.data), rownames(obj@meta.data))) + expr.data = expr.data %>% + bind_cols(obj@meta.data) %>% + pivot_longer(cols =!c(colnames(obj@meta.data)) , names_to='FEATURE', values_to='EXPRS') %>% + data.frame() + + expr.data.l = expr.data %>% + filter(.data[[col]] %in% !!col.lvl) %>% + group_by(FEATURE) %>% + group_split() + + # grp = expr.data.l[[1]] + get_plots = function(grp){ + + keep = names(table(grp$orig.ident)[table(grp$orig.ident) > 1]) + grp = grp[grp$orig.ident %in% keep, ] + grp = droplevels(grp) + + newlevels = grp %>% + group_by(orig.ident, RESPONSE_CONSENSUS) %>% + summarise(AVE_EXPRS = median(EXPRS)) %>% + arrange(-AVE_EXPRS) %>% + ungroup %>% select(orig.ident) %>% unlist + grp = grp %>% mutate(orig.ident = factor(orig.ident, levels = newlevels)) + + s = levels(grp$orig.ident) + p = as.character(grp$PATIENT_ID[match(s, grp$orig.ident)]) + p = paste0(gsub("Patient 0", "P", p)) + x.labels = setNames(p, s) + + pl = + ggplot(grp, aes(x=orig.ident, y=EXPRS,fill=RESPONSE_CONSENSUS))+ + # geom_violin(linewidth = 0.2) + + geom_violin(linewidth = 0.2, scale = "width", width = .5) + + geom_jitter(shape = ".", alpha = 0.5, width = .1, show.legend = F, color='#555555')+ + scale_x_discrete(labels = x.labels) + + stat_summary(fun= "median",geom = "crossbar", width = 0.4, show.legend = F, lwd = .3)+ + scale_fill_manual(values = c("CR" = "#6699CC", "nonCR" = "#997700")) + + mytheme() + + theme( + axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), + axis.title.x=element_blank() + ) + + annotate("text", x=Inf, y = Inf, label = label, vjust=1.6, hjust=1.4, size = 3) + + ylab("Expression") + labs(fill = NULL) + } + + pl.l = lapply(expr.data.l, get_plots) + names(pl.l) = unlist(lapply(expr.data.l, function(x){x$FEATURE[1]})) + pl.l +} \ No newline at end of file diff --git a/code/helper/italk_utilities.R b/code/helper/italk_utilities.R new file mode 100644 index 0000000..ceeda51 --- /dev/null +++ b/code/helper/italk_utilities.R @@ -0,0 +1,94 @@ +# Function from iTalk with modified font size +LRPlot2<-function(data,datatype,gene_col=NULL,transparency=0.5,link.arr.lwd=1,link.arr.lty=NULL,link.arr.col=NULL,link.arr.width=NULL, + link.arr.type=NULL,facing='clockwise',cell_col=NULL,print.cell=TRUE,track.height_1=uh(2,'mm'),track.height_2=uh(12,'mm'), + annotation.height_1=0.01,annotation.height_2=0.01,text.vjust = '0.4cm',gene_size=1.2, celltype_size=1.5,...){ + cell_group<-unique(c(data$cell_from,data$cell_to)) + genes<-c(structure(data$ligand,names=data$cell_from),structure(data$receptor,names=data$cell_to)) + genes<-genes[!duplicated(paste(names(genes),genes))] + genes<-genes[order(names(genes))] + if(is.null(link.arr.lty)){ + if(datatype=='mean count'){ + link.arr.lty='solid' + }else if(datatype=='DEG'){ + link.arr.lty=structure(ifelse(data$cell_from_logFC==0.0001,'dashed','solid'),names=paste(data$cell_from,data$receptor)) + }else{ + print('invalid datatype') + } + } + if(is.null(link.arr.col)){ + if(datatype=='mean count'){ + data<-data %>% mutate(link_col='black') + }else if(datatype=='DEG'){ + data<-data %>% mutate(link_col=ifelse(cell_from_logFC==0.0001,ifelse(cell_to_logFC>0,'#994455','#6699CC'), + ifelse(cell_to_logFC==0.0001,ifelse(cell_from_logFC>0,'#994455','#6699CC'), + ifelse(cell_from_logFC>0,ifelse(cell_to_logFC>0,'#994455','#dfc27d'), + ifelse(cell_to_logFC>0,'#9933ff','#6699CC'))))) + }else{ + print('invalid datatype') + } + }else{ + data$link_col=link.arr.col + } + if(is.null(link.arr.type)){ + if(datatype=='mean count'){ + link.arr.type='triangle' + }else if(datatype=='DEG'){ + link.arr.type=structure(ifelse(data$cell_to_logFC==0.0001,'ellipse','triangle'),names=paste(data$cell_from,data$receptor)) + }else{ + print('invalid datatype') + } + } + if(is.null(gene_col)){ + comm_col<-structure(c('#99ff99','#99ccff','#ff9999','#ffcc99'),names=c('other','cytokine','checkpoint','growth factor')) + gene_col<-structure(c(comm_col[data$comm_type],rep('#073c53',length(data$receptor))),names=c(data$ligand,data$receptor)) + } + if(is.null(cell_col)){ + cell_col<-structure(randomColor(count=length(unique(names(genes))),luminosity='dark'),names=unique(names(genes))) + } + if(is.null(link.arr.lwd)){ + data<-data %>% mutate(arr_width=1) + }else if(max(abs(link.arr.lwd))-min(abs(link.arr.lwd))==0 && all(link.arr.lwd!=0.0001)){ + data<-data %>% mutate(arr_width=ifelse(abs(link.arr.lwd<5),abs(link.arr.lwd),5)) + }else{ + data<-data %>% mutate(arr_width=ifelse(link.arr.lwd==0.0001,2,1+5/(max(abs(link.arr.lwd))-min(abs(link.arr.lwd)))*(abs(link.arr.lwd)-min(abs(link.arr.lwd))))) + } + if(length(cell_group)!=1){ + gap.degree <- do.call("c", lapply(table(names(genes)), function(i) c(rep(1, i-1), 8))) + }else{ + gap.degree <- do.call("c", lapply(table(names(genes)), function(i) c(rep(1, i)))) + } + circos.par(gap.degree = gap.degree) + if(length(gene_col)==1){ + grid.col=gene_col + }else{ + grid.col=gene_col[genes] + names(grid.col)<-paste(names(genes),genes) + } + if(is.null(link.arr.width)){ + data<-data %>% mutate(link.arr.width=data$arr_width/10) + }else if(max(abs(link.arr.width))-min(abs(link.arr.width))==0 && all(link.arr.width!=0.0001)){ + data<-data %>% mutate(link.arr.width=ifelse(abs(link.arr.width)<0.5,abs(link.arr.width),0.5)) + }else{ + data<-data %>% mutate(link.arr.width=ifelse(link.arr.width==0.0001,0.2,(1+5/(max(abs(link.arr.width))-min(abs(link.arr.width)))*(abs(link.arr.width)-min(abs(link.arr.width))))/10)) + } + chordDiagram(as.data.frame(cbind(paste(data$cell_from,data$ligand),paste(data$cell_to,data$receptor))), order=paste(names(genes),genes), + grid.col=grid.col,transparency=transparency,directional=1,direction.type='arrows',link.arr.lwd=data$arr_width,link.arr.lty=link.arr.lty, + link.arr.type=link.arr.type,link.arr.width=data$link.arr.width,link.arr.col=data$link_col,col='#00000000',annotationTrack=c('grid'),preAllocateTracks = list( + list(track.height = track.height_1),list(track.height = track.height_2)),annotationTrackHeight = c(annotation.height_1,annotation.height_2),...) + + circos.trackPlotRegion(track.index = 2, panel.fun = function(x, y) { + xlim = get.cell.meta.data("xlim") + ylim = get.cell.meta.data("ylim") + sector.index = genes[get.cell.meta.data("sector.numeric.index")] + circos.text(mean(xlim),mean(ylim),sector.index, col = "black", cex = gene_size, facing = facing, niceFacing = TRUE) #Change fontsize here + }, bg.border = 0) + + if(print.cell){ + for(c in unique(names(genes))) { + gene = as.character(genes[names(genes) == c]) + highlight.sector(sector.index = paste(c,gene), track.index = 1, col = ifelse(length(cell_col)==1,cell_col,cell_col[c]), text = c, + text.vjust = text.vjust, niceFacing = TRUE,lwd=1, cex = celltype_size) + } + } + circos.clear() +} \ No newline at end of file diff --git a/code/helper/projectTils.R b/code/helper/projectTils.R new file mode 100644 index 0000000..6129852 --- /dev/null +++ b/code/helper/projectTils.R @@ -0,0 +1,160 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# https://github.com/carmonalab/ProjecTILs/blob/master/R/main.R +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>q>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +ProjecTILs.classifier_custom <- function( + query, ref=NULL, + filter.cells = TRUE, + split.by = NULL, + reduction="pca", + ndim=NULL, k=20, + labels.col="functional.cluster", + ncores = threads, + ... +) { + + fast.umap.predict <- TRUE + # only needed if we want to predict labels based on UMAP neighbors + if (reduction=="umap") { fast.umap.predict <- FALSE } + + if(is.list(query)) { + stop("Query must be a single Seurat object") + } + + current.labs <- NULL + if (labels.col %in% colnames(query[[]])) { + current.labs <- query[[labels.col]] + } + + if (!is.null(split.by)) { + if (!split.by %in% colnames(query[[]])) { + stop(sprintf("No grouping variable %s available in metadata", split.by)) + } + q = Split_Object(query, split.by = split.by, threads = ncores) + } else { + q <- query + } + + # Run projection + q <- ProjecTILs::make.projection(query=q, ref=ref, filter.cells=filter.cells, fast.umap.predict = fast.umap.predict, ncores = ncores, ...) + + if(!is.list(q)) { + q <- list(query=q) + } + + # Cell type classification + q <- lapply(q, function(x) { + ProjecTILs::cellstate.predict(ref=ref, query=x, reduction=reduction, ndim=ndim, k=k, labels.col = labels.col) + }) + + # Merge annotations + labs = lapply(q, function(x){ + x = x@meta.data[, c("functional.cluster", "functional.cluster.conf")] + x$BARCODE = rownames(x) + x + }) + labs = do.call("rbind", labs) + rownames(labs) = labs$BARCODE + labs$BARCODE = NULL + query = AddMetaData(query, labs) + query@meta.data$functional.cluster.conf[is.na(query@meta.data$functional.cluster.conf)] = 0 + query +} + +ProjecTILs_worflow <- function( + se.obj = NULL, + threads = ncores +) { + + print("Split Seurat") + obj.l = Split_Object(se.obj, split.by = "orig.ident", threads = threads) + idents = names(obj.l) + names(idents) = idents + + bpparam = BiocParallel::MulticoreParam(workers = threads) + + print("Filtering out samples with too few CD4 cells") + nbr.cd4.cells = BiocParallel::bplapply(idents, function(cl) { + se = scGate( + data = obj.l[[cl]], model = scGate_model.cd4, assay = "RNA", + additional.signatures = cell.cycle.obj[["human"]] + ) + nrow(subset(se@meta.data, is.pure == "Pure")) + }, BPPARAM = bpparam) + + print("Filtering out samples with too few CD8 cells") + nbr.cd8.cells = BiocParallel::bplapply(idents, function(cl) { + se = scGate( + data = obj.l[[cl]], model = scGate_model.cd8, assay = "RNA", + additional.signatures = cell.cycle.obj[["human"]] + ) + nrow(subset(se@meta.data, is.pure == "Pure")) + }, BPPARAM = bpparam) + + keep.cd4 = unlist(nbr.cd4.cells)[!unlist(nbr.cd4.cells) < 20] + keep.cd8 = unlist(nbr.cd8.cells)[!unlist(nbr.cd8.cells) < 20] + + celltypes = data.frame(row.names = rownames(se.obj@meta.data)) + + if(length(keep.cd4) > 0){ + se.obj.cd4 = subset(se.obj, subset = orig.ident %in% names(keep.cd4)) + se.obj.cd4@meta.data = droplevels(se.obj.cd4@meta.data) + + # Run ProjecTILs classification + print("ProjecTILs: CD4") + project.tils.cd4 <- ProjecTILs.classifier_custom( + query = se.obj.cd4, + ref = ref.cd4, + filter.cells = T, + split.by = 'orig.ident', + ncores = threads + ) + + celltypes$CELLTYPES_CD4 = project.tils.cd4$functional.cluster[match(rownames(celltypes), rownames(project.tils.cd4@meta.data))] + celltypes$CELLTYPES_CD4_CONF = project.tils.cd4$functional.cluster.conf[match(rownames(celltypes), rownames(project.tils.cd4@meta.data))] + celltypes$CELLTYPES_CD4_CONF[celltypes$CELLTYPES_CD4_CONF == 0] = NA + + } else { + celltypes$CELLTYPES_CD4 = NA + celltypes$CELLTYPES_CD4_CONF = NA + } + + if(length(keep.cd8) > 0){ + se.obj.cd8 = subset(se.obj, subset = orig.ident %in% names(keep.cd8)) + se.obj.cd8@meta.data = droplevels(se.obj.cd8@meta.data) + + print("ProjecTILs: CD8") + project.tils.cd8 <- ProjecTILs.classifier_custom( + query = se.obj.cd8, + ref = ref.cd8, + filter.cells = T, + split.by = 'orig.ident', + ncores = threads + ) + + celltypes$CELLTYPES_CD8 = project.tils.cd8$functional.cluster[match(rownames(celltypes), rownames(project.tils.cd8@meta.data))] + celltypes$CELLTYPES_CD8_CONF = project.tils.cd8$functional.cluster.conf[match(rownames(celltypes), rownames(project.tils.cd8@meta.data))] + celltypes$CELLTYPES_CD8_CONF[celltypes$CELLTYPES_CD8_CONF == 0] = NA + + } else { + celltypes$CELLTYPES_CD8 = NA + celltypes$CELLTYPES_CD8_CONF = NA + } + + celltypes[!is.na(celltypes$CELLTYPES_CD8) & !is.na(celltypes$CELLTYPES_CD4), ] = NA + celltypes.merged = dplyr::coalesce(celltypes$CELLTYPES_CD8, celltypes$CELLTYPES_CD4) + celltypes.merged.conf = dplyr::coalesce(celltypes$CELLTYPES_CD8_CONF, celltypes$CELLTYPES_CD4_CONF) + + se.obj@meta.data$ProjecTILs = celltypes.merged + se.obj@meta.data$ProjecTILs_CONF = celltypes.merged.conf + se.obj$ProjecTILs[grepl("^CD4", se.obj$ProjecTILs) & se.obj$CD4CD8_BY_EXPRS == "CD4-CD8+"] = NA + se.obj$ProjecTILs[grepl("^CD8", se.obj$ProjecTILs) & se.obj$CD4CD8_BY_EXPRS == "CD4+CD8-"] = NA + se.obj$ProjecTILs_CONF[is.na(se.obj$ProjecTILs)] = NA + se.obj$ProjecTILs_LIN = case_when( + grepl("^CD8", se.obj$ProjecTILs) ~ "CD8", + grepl("^CD4", se.obj$ProjecTILs) ~ "CD4" + ) + se.obj$ProjecTILs[is.na(se.obj$ProjecTILs)] = "Not Estimable" + + se.obj + +} diff --git a/code/helper/seurat_utilities.R b/code/helper/seurat_utilities.R new file mode 100644 index 0000000..f3a9930 --- /dev/null +++ b/code/helper/seurat_utilities.R @@ -0,0 +1,311 @@ +# Pseudobulk feature expression by identity class +# +# Returns a representative expression value for each identity class +# +# @param object Seurat object +# @param pb.method Whether to 'average' (default) or 'aggregate' expression levels +# @param assays Which assays to use. Default is all assays +# @param features Features to analyze. Default is all features in the assay +# @param return.seurat Whether to return the data as a Seurat object. Default is FALSE +# @param group.by Categories for grouping (e.g, ident, replicate, celltype); 'ident' by default +# @param add.ident (Deprecated) Place an additional label on each cell prior to pseudobulking +# (very useful if you want to observe cluster pseudobulk values, separated by replicate, for example) +# @param slot Slot(s) to use; if multiple slots are given, assumed to follow +# the order of 'assays' (if specified) or object's assays +# @param verbose Print messages and show progress bar +# @param ... Arguments to be passed to methods such as \code{\link{CreateSeuratObject}} +# +# @return Returns a matrix with genes as rows, identity classes as columns. +# If return.seurat is TRUE, returns an object of class \code{\link{Seurat}}. +# +#' @importFrom Matrix rowMeans sparse.model.matrix +#' @importFrom stats as.formula +# @export +# +# @examples +# data("pbmc_small") +# head(PseudobulkExpression(object = pbmc_small)) +# +PseudobulkExpression_custom <- function( + object, + pb.method = 'average', + assays = NULL, + features = NULL, + return.seurat = FALSE, + group.by = 'ident', + add.ident = NULL, + slot = 'data', + verbose = TRUE, + ... +) { + CheckDots(..., fxns = 'CreateSeuratObject') + + message( + "This is a modified version (MiR) of the function Seurat::PseudobulkExpression. + If slot == \"counts\" and return.seurat = TRUE, the matrix is not logarithmized + and put into the slot \"data\"." + ) + + library(Matrix) + library(Seurat) + + if (!is.null(x = add.ident)) { + .Deprecated(msg = "'add.ident' is a deprecated argument, please use the 'group.by' argument instead") + group.by <- c('ident', add.ident) + } + if (!(pb.method %in% c('average', 'aggregate'))) { + stop("'pb.method' must be either 'average' or 'aggregate'") + } + object.assays <- FilterObjects(object = object, classes.keep = 'Assay') + assays <- assays %||% object.assays + if (!all(assays %in% object.assays)) { + assays <- assays[assays %in% object.assays] + if (length(x = assays) == 0) { + stop("None of the requested assays are present in the object") + } else { + warning("Requested assays that do not exist in object. Proceeding with existing assays only.") + } + } + if (length(x = slot) == 1) { + slot <- rep_len(x = slot, length.out = length(x = assays)) + } else if (length(x = slot) != length(x = assays)) { + stop("Number of slots provided does not match number of assays") + } + data <- FetchData(object = object, vars = rev(x = group.by)) + data <- data[which(rowSums(x = is.na(x = data)) == 0), , drop = F] + if (nrow(x = data) < ncol(x = object)) { + message("Removing cells with NA for 1 or more grouping variables") + object <- subset(x = object, cells = rownames(x = data)) + } + for (i in 1:ncol(x = data)) { + data[, i] <- as.factor(x = data[, i]) + } + num.levels <- sapply( + X = 1:ncol(x = data), + FUN = function(i) { + length(x = levels(x = data[, i])) + } + ) + if (any(num.levels == 1)) { + message(paste0("The following grouping variables have 1 value and will be ignored: ", + paste0(colnames(x = data)[which(num.levels <= 1)], collapse = ", "))) + group.by <- colnames(x = data)[which(num.levels > 1)] + data <- data[, which(num.levels > 1), drop = F] + } + if (ncol(x = data) == 0) { + message("All grouping variables have 1 value only. Computing across all cells.") + category.matrix <- matrix( + data = 1, + nrow = ncol(x = object), + dimnames = list(Cells(x = object), 'all') + ) + if (pb.method == 'average') { + category.matrix <- category.matrix / sum(category.matrix) + } + } else { + category.matrix <- sparse.model.matrix(object = as.formula( + object = paste0( + '~0+', + paste0( + "data[,", + 1:length(x = group.by), + "]", + collapse = ":" + ) + ) + )) + colsums <- colSums(x = category.matrix) + category.matrix <- category.matrix[, colsums > 0] + colsums <- colsums[colsums > 0] + if (pb.method == 'average') { + category.matrix <- Sweep( + x = category.matrix, + MARGIN = 2, + STATS = colsums, + FUN = "/") + } + colnames(x = category.matrix) <- sapply( + X = colnames(x = category.matrix), + FUN = function(name) { + name <- gsub(pattern = "data\\[, [1-9]*\\]", replacement = "", x = name) + return(paste0(rev(x = unlist(x = strsplit(x = name, split = ":"))), collapse = "_")) + }) + } + data.return <- list() + for (i in 1:length(x = assays)) { + data.use <- GetAssayData( + object = object, + assay = assays[i], + slot = slot[i] + ) + features.to.avg <- features %||% rownames(x = data.use) + if (inherits(x = features, what = "list")) { + features.to.avg <- features[i] + } + if (IsMatrixEmpty(x = data.use)) { + warning( + "The ", slot[i], " slot for the ", assays[i], + " assay is empty. Skipping assay.", immediate. = TRUE, call. = FALSE) + next + } + bad.features <- setdiff(x = features.to.avg, y = rownames(x = data.use)) + if (length(x = bad.features) > 0) { + warning( + "The following ", length(x = bad.features), + " features were not found in the ", assays[i], " assay: ", + paste(bad.features, collapse = ", "), call. = FALSE, immediate. = TRUE) + } + features.assay <- intersect(x = features.to.avg, y = rownames(x = data.use)) + if (length(x = features.assay) > 0) { + + data.use <- data.use[features.assay, ] + } else { + warning("None of the features specified were found in the ", assays[i], + " assay.", call. = FALSE, immediate. = TRUE) + next + } + if (slot[i] == 'data') { + data.use <- expm1(x = data.use) + if (any(data.use == Inf)) { + warning("Exponentiation yielded infinite values. `data` may not be log-normed.") + } + } + data.return[[i]] <- as.matrix(x = (data.use %*% category.matrix)) + names(x = data.return)[i] <- assays[[i]] + } + if (return.seurat) { + if (slot[1] == 'scale.data') { + na.matrix <- data.return[[1]] + na.matrix[1:length(x = na.matrix)] <- NA + toRet <- CreateSeuratObject( + counts = na.matrix, + project = if (pb.method == "average") "Average" else "Aggregate", + assay = names(x = data.return)[1], + check.matrix = FALSE, + ... + ) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[1], + slot = "counts", + new.data = matrix() + ) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[1], + slot = "data", + new.data = na.matrix + ) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[1], + slot = "scale.data", + new.data = data.return[[1]] + ) + } else { + toRet <- CreateSeuratObject( + counts = data.return[[1]], + project = if (pb.method == "average") "Average" else "Aggregate", + assay = names(x = data.return)[1], + check.matrix = FALSE, + ... + ) + # toRet <- SetAssayData( + # object = toRet, + # assay = names(x = data.return)[1], + # slot = "data", + # new.data = log1p(x = as.matrix(x = data.return[[1]])) + # ) + } + #for multimodal data + if (length(x = data.return) > 1) { + for (i in 2:length(x = data.return)) { + if (slot[i] == 'scale.data') { + na.matrix <- data.return[[i]] + na.matrix[1:length(x = na.matrix)] <- NA + toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = na.matrix, check.matrix = FALSE) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[i], + slot = "counts", + new.data = matrix() + ) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[i], + slot = "data", + new.data = na.matrix + ) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[i], + slot = "scale.data", + new.data = as.matrix(x = data.return[[i]]) + ) + } else { + toRet[[names(x = data.return)[i]]] <- CreateAssayObject(counts = data.return[[i]], check.matrix = FALSE) + toRet <- SetAssayData( + object = toRet, + assay = names(x = data.return)[i], + slot = "data", + new.data = log1p(x = as.matrix(x = data.return[[i]])) + ) + } + + } + } + if (DefaultAssay(object = object) %in% names(x = data.return)) { + DefaultAssay(object = toRet) <- DefaultAssay(object = object) + if(slot == "counts" & return.seurat == T) { + message("No scaling is performed. This is a modified version (MiR) of the function Seurat::PseudobulkExpression.") + } else { + if (slot[which(DefaultAssay(object = object) %in% names(x = data.return))[1]] != 'scale.data') { + toRet <- ScaleData(object = toRet, verbose = verbose) + } + } + + } + if ('ident' %in% group.by) { + first.cells <- c() + for (i in 1:ncol(x = category.matrix)) { + first.cells <- c(first.cells, Position(x = category.matrix[,i], f = function(x) {x > 0})) + } + Idents(object = toRet) <- Idents(object = object)[first.cells] + } + return(toRet) + } else { + return(data.return) + } +} + +# Sweep out array summaries +# +# Reimplmentation of \code{\link[base]{sweep}} to maintain compatability with +# both R 3.X and 4.X +# +# @inheritParams base::sweep +# @param x an array. +# +# @seealso \code{\link[base]{sweep}} +# +Sweep <- function(x, MARGIN, STATS, FUN = '-', check.margin = TRUE, ...) { + if (any(grepl(pattern = 'X', x = names(x = formals(fun = sweep))))) { + return(sweep( + X = x, + MARGIN = MARGIN, + STATS = STATS, + FUN = FUN, + check.margin = check.margin, + ... + )) + } else { + return(sweep( + x = x, + MARGIN = MARGIN, + STATS = STATS, + FUN = FUN, + check.margin = check.margin, + ... + )) + } +} diff --git a/code/helper/styles.R b/code/helper/styles.R new file mode 100644 index 0000000..d0696be --- /dev/null +++ b/code/helper/styles.R @@ -0,0 +1,153 @@ +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Colors +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +.inst = c("ggthemes", "scales") %in% installed.packages() +if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") +} + +colorblind_vector <- colorRampPalette(c("#FF4B20", "#FFB433", "#C6FDEC", "#7AC5FF", "#0348A6")) +colors_use.10 = ggthemes::tableau_color_pal("Tableau 10")(10) +colors_use.20 = ggthemes::tableau_color_pal("Tableau 20")(20) +colors_stata = ggthemes::stata_pal("s2color")(15) +cell.cylce.col = c("#004488", "#DDAA33", "#BB5566", "black") +names(cell.cylce.col) = c("G1", "S", "G2M", "-") + +cont.col = c( + "#412856", "#412856", "#344174", "#386293", "#5686AC", + "#85A9C2", "#B5C0C7", "#f0f0f0", "#D8A88D", "#CC8864", + "#B2613C", "#903A22", "#721F1E", "#5E1529", "#5E1529" +) + +ct.col = c( + "Plasma(blast)" = "#7C2529", + "Plasmablast" = "#7C2529", + "Plasma cell" = "#7C2529", + "B-Cell" = "#E18A8D", + "NK" = "#BC8400", + "NK_CD56bright" = "#BC8400", + "CD56 bright NK" = "#BC8400", + "CD4 T-Cell" = "#e8d725", + "CD8 T-Cell" = "#AFA10D", + "gdT-Cell" = "#20581C", + "T-Cell (cycling)" = "#CC3311", + "Mono CD14" = "#93aeba", + "CD14 Mono" = "#93aeba", + "Mono CD16" = "#4B859F", + "CD16 Mono" = "#4B859F", + "cDC" = "#9C9BDB", + "pDC" = "#194573", + "other DC" = "#D1BBD7", + "Macrophage" = "black", + "Erythrocyte" = "#B281A6", + "Platelet" = "#AA4499", + "Progenitor" = "#555555", + "Other" = "black", + "Cycling" = "grey" +) + +cd8.col = c("#0077BB", "#33BBEE", "#009988", "#EE7733", "#EE6677", "#EE3377", "#997700", "#994455") +names(cd8.col) = c("CD8.NaiveLike", "CD8.CM", "CD8.EM", "CD8.TEMRA", "CD8.TPEX", "CD8.TEX", "CD8.MAIT", "CD8.Cycling") +cd4.col = c("#b7d2e0", "#da6f6f", "#72b28a", "#e5bfaf", "#aca6e0", "#f5d39f", "#fdbfd4", "#994455") +names(cd4.col) = c("CD4.NaiveLike", "CD4.CTL_EOMES", "CD4.CTL_GNLY", "CD4.CTL_Exh", "CD4.Tfh", "CD4.Th17", "CD4.Treg", "CD4.Cycling") +ct.misc = c("#BBBBBB", "#555555", "#BBBBBB", "#555555", "#332288", "#DDDDDD", "#9e4f6c", "#4f2535", "#555555", "#CCDDAA", "#994455", "#DDDDDD") +names(ct.misc) = c("CD4.other", "CD8.other", "CD4", "CD8", "NaiveLike", "NE", "S", "G2M", "T_myeloid", "gdT", "Cycling", "Not Estimable") +til.col = c(cd8.col, cd4.col, ct.misc) + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# ggplot theme +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +mytheme = function(base_size = 8, base_family = "") { + half_line <- base_size/2 + theme_light(base_size = base_size, base_family = base_family) + + theme( + panel.grid.major = element_blank(), + panel.grid.minor = element_blank(), + panel.background = element_rect(fill = "transparent",colour = NA), + plot.background = element_rect(fill = "transparent",colour = NA), + axis.ticks.length = unit(half_line / 2.2, "pt"), + axis.ticks = element_line(colour = "black"), + strip.background = element_rect(fill = NA, colour = NA), + strip.text.x = element_text(size = rel(1), colour = "black"), + strip.text.y = element_text(size = rel(1), colour = "black"), + strip.text = element_text(size = rel(1), colour = "black"), + axis.text = element_text(size = rel(1), colour = "black"), + axis.title = element_text(size = rel(1), colour = "black"), + legend.title = element_text(colour = "black", size = rel(1)), + panel.border = element_rect(fill = NA, colour = "black", linewidth = .3), + legend.key.size = unit(1, "lines"), + legend.text = element_text(size = rel(1), colour = "black"), + legend.key = element_rect(colour = NA, fill = NA), + legend.background = element_rect(colour = NA, fill = NA), + plot.title = element_text(hjust = 0, face = "plain", colour = "black", size = rel(1)), + plot.subtitle = element_text(colour = "black", size = rel(.85)) + ) +} + +mytheme_grid = function(base_size = 8, base_family = "") { + half_line <- base_size/2 + theme_light(base_size = base_size, base_family = base_family) + + theme( + panel.background = element_rect(fill = "transparent",colour = NA), + plot.background = element_rect(fill = "transparent",colour = NA), + axis.ticks.length = unit(half_line / 2.2, "pt"), + axis.ticks = element_line(colour = "black"), + strip.background = element_rect(fill = NA, colour = NA), + strip.text.x = element_text(size = rel(1), colour = "black"), + strip.text.y = element_text(size = rel(1), colour = "black"), + strip.text = element_text(size = rel(1), colour = "black"), + axis.text = element_text(size = rel(1), colour = "black"), + axis.title = element_text(size = rel(1), colour = "black"), + legend.title = element_text(colour = "black", size = rel(1)), + panel.border = element_rect(fill = NA, colour = "black", linewidth = .3), + legend.key.size = unit(1, "lines"), + legend.text = element_text(size = rel(1), colour = "black"), + legend.key = element_rect(colour = NA, fill = NA), + legend.background = element_rect(colour = NA, fill = NA), + plot.title = element_text(hjust = 0, face = "plain", colour = "black", size = rel(1)), + plot.subtitle = element_text(colour = "black", size = rel(.85)) + ) +} + +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +# Make grandient for ggplot (background) +# >>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>> +make_gradient <- function(deg = 45, n = 100, cols = blues9) { + + .cran_packages = c("grid", "ggplot2","RColorBrewer") + .inst = .cran_packages %in% installed.packages() + if (any(!.inst)) { + install.packages(.cran_packages[!.inst], repos = "http://cran.rstudio.com/") + } + for (pack in .cran_packages) { + suppressMessages(library( + pack, + quietly = TRUE, + verbose = FALSE, + character.only = TRUE + )) + } + + cols <- colorRampPalette(cols)(n + 1) + rad <- deg / (180 / pi) + mat <- matrix( + data = rep(seq(0, 1, length.out = n) * cos(rad), n), + byrow = TRUE, + ncol = n + ) + + matrix( + data = rep(seq(0, 1, length.out = n) * sin(rad), n), + byrow = FALSE, + ncol = n + ) + mat <- mat - min(mat) + mat <- mat / max(mat) + mat <- 1 + mat * n + mat <- matrix(data = cols[round(mat)], ncol = n) + grid::rasterGrob( + image = mat, + width = unit(1, "npc"), + height = unit(1, "npc"), + interpolate = TRUE + ) +} diff --git a/code/supplementary_file/.vscode/ltex.dictionary.en-US.txt b/code/supplementary_file/.vscode/ltex.dictionary.en-US.txt new file mode 100644 index 0000000..67a7f83 --- /dev/null +++ b/code/supplementary_file/.vscode/ltex.dictionary.en-US.txt @@ -0,0 +1 @@ +pseudobulk diff --git a/code/supplementary_file/bibliography-mimosis.tex b/code/supplementary_file/bibliography-mimosis.tex new file mode 100644 index 0000000..76834be --- /dev/null +++ b/code/supplementary_file/bibliography-mimosis.tex @@ -0,0 +1,132 @@ +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Some adjustments to make the bibliography more clean +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +% The subsequent commands do the following: +% - Removing the month field from the bibliography +% - Fixing the Oxford commma +% - Suppress the "in" for journal articles +% - Remove the parentheses of the year in an article +% - Delimit volume and issue of an article by a colon ":" instead of +% a dot "" +% - Use commas to separate the location of publishers from their name +% - Remove the abbreviation for technical reports +% - Display the label of bibliographic entries without brackets in the +% bibliography +% - Ensure that DOIs are followed by a non-breakable space +% - Use hair spaces between initials of authors +% - Make the font size of citations smaller +% - Fixing ordinal numbers (1st, 2nd, 3rd, and so) on by using +% superscripts + +% Remove the month field from the bibliography. It does not serve a good +% purpose, I guess. And often, it cannot be used because the journals +% have some crazy issue policies. +\AtEveryBibitem{\clearfield{month}} +\AtEveryCitekey{\clearfield{month}} + +% Fixing the Oxford comma. Not sure whether this is the proper solution. +% More information is available under [1] and [2]. +% +% [1] http://tex.stackexchange.com/questions/97712/biblatex-apa-style-is-missing-a-comma-in-the-references-why +% [2] http://tex.stackexchange.com/questions/44048/use-et-al-in-biblatex-custom-style +% +\AtBeginBibliography{% + \renewcommand*{\finalnamedelim}{% + \ifthenelse{\value{listcount} > 2}{% + \addcomma + \addspace + \bibstring{and}% + }{% + \addspace + \bibstring{and}% + } + } +} + +% Suppress "in" for journal articles. This is unnecessary in my opinion +% because the journal title is typeset in italics anyway. +\renewbibmacro{in:}{% + \ifentrytype{article} + {% + }% + % else + {% + \printtext{\bibstring{in}\intitlepunct}% + }% +} + +% Remove the parentheses for the year in an article. This removes a lot +% of undesired parentheses in the bibliography, thereby improving the +% readability. Moreover, it makes the look of the bibliography more +% consistent. +\renewbibmacro*{issue+date}{% + \setunit{\addcomma\space} + \iffieldundef{issue} + {\usebibmacro{date}} + {\printfield{issue}% + \setunit*{\addspace}% + \usebibmacro{date}}% + \newunit} + +% Delimit the volume and the number of an article by a colon instead of +% by a dot, which I consider to be more readable. +\renewbibmacro*{volume+number+eid}{% + \printfield{volume}% + \setunit*{\addcolon}% + \printfield{number}% + \setunit{\addcomma\space}% + \printfield{eid}% +} + +% Do not use a colon for the publisher location. Instead, connect +% publisher, location, and date via commas. +\renewbibmacro*{publisher+location+date}{% + \printlist{publisher}% + \setunit*{\addcomma\space}% + \printlist{location}% + \setunit*{\addcomma\space}% + \usebibmacro{date}% + \newunit% +} + +% Ditto for other entry types. +\renewbibmacro*{organization+location+date}{% + \printlist{location}% + \setunit*{\addcomma\space}% + \printlist{organization}% + \setunit*{\addcomma\space}% + \usebibmacro{date}% + \newunit% +} + +% Display the label of a bibliographic entry in bare style, without any +% brackets. I like this more than the default. +% +% Note that this is *really* the proper and official way of doing this. +\DeclareFieldFormat{labelnumberwidth}{#1\adddot} + +% Ensure that DOIs are followed by a non-breakable space. +\DeclareFieldFormat{doi}{% + \mkbibacro{DOI}\addcolon\addnbspace + \ifhyperref + {\href{http://dx.doi.org/#1}{\nolinkurl{#1}}} + % + {\nolinkurl{#1}} +} + +% Use proper hair spaces between initials as suggested by Bringhurst and +% others. +\renewcommand*\bibinitdelim {\addnbthinspace} +\renewcommand*\bibnamedelima{\addnbthinspace} +\renewcommand*\bibnamedelimb{\addnbthinspace} +\renewcommand*\bibnamedelimi{\addnbthinspace} + +% Make the font size of citations smaller. Depending on your selected +% font, you might not need this. +\renewcommand*{\citesetup}{% + \biburlsetup + \small +} + +\DeclareLanguageMapping{english}{english-mimosis} diff --git a/code/supplementary_file/cleanlatexjunk.sh b/code/supplementary_file/cleanlatexjunk.sh new file mode 100644 index 0000000..26936ea --- /dev/null +++ b/code/supplementary_file/cleanlatexjunk.sh @@ -0,0 +1,83 @@ +#!/usr/bin/env bash + +# CleanLatexJunk v1.0 +# by Christoph Graumann +# graumann@cs.tum.edu +# Licensed under GPLv3 + +usage() +{ + echo "Usage: `basename $0` [-p latex_file] [-f] " + exit -1 +} + +path=${!#} + +# Require folder name +if [ -z $path ] || [ ! -d $path ]; then + usage + exit 1 +fi + + + # default values + thefile=${path%%/}/* + name=false + docheck=true + + while getopts ":fp:" opt; do + case $opt in + f) + echo "Removing latex junk without security check..." >&2 + docheck=false + ;; + p) + echo "Removing junk-files for $OPTARG" >&2 + thefile=${path%%/}/${OPTARG%\.tex} + name=true + ;; + \?) + echo "Invalid option: -$OPTARG" >&2 + usage + exit 1 + ;; + :) + echo "Option -$OPTARG requires an argument." >&2 + usage + exit 1 + ;; + esac + done + +# Ensure the argument is a .tex file and exists. +if [ $name == true ] && [ ! -f $thefile.tex ]; then + echo "Can't find ${thefile}.tex" + usage +fi + +if [ $docheck == true ]; then + echo "CAUTION: This script will delete files with certain extensions without further prompt. If you confirm, please enter [Y] (upper-case y): " + read confirm + if [ "$confirm" != "Y" ]; then + echo "-- aborted ---" + exit 1 + fi +fi + + # Remove leftovers from latex + rm -f $thefile.aux $thefile.log $thefile.out + # Remove leftovers from bibtex + rm -f $thefile.bbl $thefile.blg $thefile.bcf + # Remove leftovers from glossaries + rm -f $thefile.glg $thefile.gls $thefile.glo + # Remove leftovers from lists + rm -f $thefile.lof $thefile.lot $thefile.toc + # Remove leftovers from minitoc + rm -f $thefile.mtc* $thefile.maf + # Remove other stuff + rm -f $thefile.run.xml + rm -f $thefile.acn $thefile.acr $thefile.alg + rm -f $thefile.ist $thefile.synctex* $thefile.alg + + echo "Latex junk files successfully removed." + exit 0 \ No newline at end of file diff --git a/code/supplementary_file/english-mimosis.lbx b/code/supplementary_file/english-mimosis.lbx new file mode 100644 index 0000000..7c891c7 --- /dev/null +++ b/code/supplementary_file/english-mimosis.lbx @@ -0,0 +1,26 @@ +\ProvidesFile{english-mimosis.lbx} +\InheritBibliographyExtras{english} + +% Do not abbreviate "technical report". +\DeclareBibliographyStrings{% + inherit = {english}, + techreport = {{technical report}{technical report}}, +} + +% Use superscripts whenever appropriate and possible +\DeclareBibliographyExtras{% + \protected\def\mkbibordinal#1{% + \begingroup% + \@tempcnta0#1\relax\number\@tempcnta% + \@whilenum\@tempcnta>100\do{\advance\@tempcnta-100\relax}% + \ifnum\@tempcnta>20% + \@whilenum\@tempcnta>9\do{\advance\@tempcnta-10\relax}% + \fi% + \mkbibsuperscript{\ifcase\@tempcnta th\or st\or nd\or rd\else th\fi}% + \endgroup}% + \protected\def\mkbibmascord{\mkbibordinal}% + \protected\def\mkbibfemord{\mkbibordinal}% + \protected\def\mkbibneutord{\mkbibordinal}% +} + +\endinput diff --git a/code/supplementary_file/references.bib b/code/supplementary_file/references.bib new file mode 100644 index 0000000..2062154 --- /dev/null +++ b/code/supplementary_file/references.bib @@ -0,0 +1,339 @@ +% Generated by Paperpile. Check out https://paperpile.com for more information. +% BibTeX export options can be customized via Settings -> BibTeX. + +@ARTICLE{Andreatta2021-yh, + title = "{UCell}: Robust and scalable single-cell gene signature scoring", + author = "Andreatta, Massimo and Carmona, Santiago J", + abstract = "UCell is an R package for evaluating gene signatures in + single-cell datasets. UCell signature scores, based on the + Mann-Whitney U statistic, are robust to dataset size and + heterogeneity, and their calculation demands less computing time + and memory than other available methods, enabling the processing + of large datasets in a few minutes even on machines with limited + computing power. UCell can be applied to any single-cell data + matrix, and includes functions to directly interact with Seurat + objects. The UCell package and documentation are available on + GitHub at https://github.com/carmonalab/UCell.", + journal = "Comput. Struct. Biotechnol. J.", + volume = 19, + pages = "3796--3798", + month = jun, + year = 2021, + keywords = "Cell type; Gene set enrichment; Gene signature; Module scoring; + Single-cell;Grieb\_et\_al\_2023", + language = "en" +} + +@ARTICLE{Dhodapkar2022-kf, + title = "Changes in Bone Marrow Tumor and Immune Cells Correlate with + Durability of Remissions Following {BCMA} {CAR} {T} Therapy in + Myeloma", + author = "Dhodapkar, Kavita M and Cohen, Adam D and Kaushal, Akhilesh and + Garfall, Alfred L and Manalo, Renee Julia and Carr, Allison R and + McCachren, Samuel S and Stadtmauer, Edward A and Lacey, Simon F + and Melenhorst, J Joseph and June, Carl H and Milone, Michael C + and Dhodapkar, Madhav V", + abstract = "UNLABELLED: Chimeric antigen-receptor (CAR) T cells lead to high + response rates in myeloma, but most patients experience recurrent + disease. We combined several high-dimensional approaches to study + tumor/immune cells in the tumor microenvironment (TME) of myeloma + patients pre- and post-B-cell maturation antigen (BCMA)-specific + CAR T therapy. Lower diversity of pretherapy T-cell receptor + (TCR) repertoire, presence of hyperexpanded clones with + exhaustion phenotype, and BAFF+PD-L1+ myeloid cells in the marrow + correlated with shorter progression-free survival (PFS) following + CAR T therapy. In contrast, longer PFS was associated with an + increased proportion of CLEC9A+ dendritic cells (DC), CD27+TCF1+ + T cells with diverse T-cell receptors, and emergence of T cells + expressing marrow-residence genes. Residual tumor cells at + initial response express stemlike genes, and tumor recurrence was + associated with the emergence of new dominant clones. These data + illustrate a dynamic interplay between endogenous T, CAR T, + myeloid/DC, and tumor compartments that affects the durability of + response following CAR T therapy in myeloma. SIGNIFICANCE: There + is an unmet need to identify determinants of durable responses + following BCMA CAR T therapy of myeloma. High-dimensional + analysis of the TME was performed to identify features of immune + and tumor cells that correlate with survival and suggest several + strategies to improve outcomes following CAR T therapy. See + related commentary by Graham and Maus, p. 478. This article is + highlighted in the In This Issue feature, p. 476.", + journal = "Blood Cancer Discov", + volume = 3, + number = 6, + pages = "490--501", + month = nov, + year = 2022, + keywords = "imSAVAR/CAR-Tcells/BCMA;Grieb\_et\_al\_2023", + language = "en" +} + +@ARTICLE{Andreatta2022-al, + title = "scGate: marker-based purification of cell types from + heterogeneous single-cell {RNA-seq} datasets", + author = "Andreatta, Massimo and Berenstein, Ariel J and Carmona, Santiago + J", + abstract = "SUMMARY: A common bioinformatics task in single-cell data + analysis is to purify a cell type or cell population of interest + from heterogeneous datasets. Here, we present scGate, an + algorithm that automatizes marker-based purification of specific + cell populations, without requiring training data or reference + gene expression profiles. scGate purifies a cell population of + interest using a set of markers organized in a hierarchical + structure, akin to gating strategies employed in flow cytometry. + scGate outperforms state-of-the-art single-cell classifiers and + it can be applied to multiple modalities of single-cell data + (e.g. RNA-seq, ATAC-seq, CITE-seq). scGate is implemented as an R + package and integrated with the Seurat framework, providing an + intuitive tool to isolate cell populations of interest from + heterogeneous single-cell datasets. AVAILABILITY AND + IMPLEMENTATION: scGate is available as an R package at + https://github.com/carmonalab/scGate + (https://doi.org/10.5281/zenodo.6202614). Several reproducible + workflows describing the main functions and usage of the package + on different single-cell modalities, as well as the code to + reproduce the benchmark, can be found at + https://github.com/carmonalab/scGate.demo + (https://doi.org/10.5281/zenodo.6202585) and + https://github.com/carmonalab/scGate.benchmark. Test data are + available at https://doi.org/10.6084/m9.figshare.16826071. + SUPPLEMENTARY INFORMATION: Supplementary data are available at + Bioinformatics online.", + journal = "Bioinformatics", + volume = 38, + number = 9, + pages = "2642--2644", + month = apr, + year = 2022, + keywords = "CAR Tcell Atlas;Grieb\_et\_al\_2023", + language = "en" +} + +@ARTICLE{Fu2020-fp, + title = "clustifyr: an {R} package for automated single-cell {RNA} + sequencing cluster classification", + author = "Fu, Rui and Gillen, Austin E and Sheridan, Ryan M and Tian, + Chengzhe and Daya, Michelle and Hao, Yue and Hesselberth, Jay R + and Riemondy, Kent A", + abstract = "Assignment of cell types from single-cell RNA sequencing + (scRNA-seq) data remains a time-consuming and error-prone + process. Current packages for identity assignment use limited + types of reference data and often have rigid data structure + requirements. We developed the clustifyr R package to leverage + several external data types, including gene expression profiles + to assign likely cell types using data from scRNA-seq, bulk + RNA-seq, microarray expression data, or signature gene lists. We + benchmark various parameters of a correlation-based approach and + implement gene list enrichment methods. clustifyr is a + lightweight and effective cell-type assignment tool developed for + compatibility with various scRNA-seq analysis workflows. + clustifyr is publicly available at + https://github.com/rnabioco/clustifyr.", + journal = "F1000Res.", + volume = 9, + pages = "223", + month = apr, + year = 2020, + keywords = "R package; Single-cell RNA sequencing; cell type classification; + gene expression + profile;Thesis/prostatrend/05\_PCa\_Atlas;Thesis/prostatrend/06\_PCa\_Tcell\_Atlas;Grieb\_et\_al\_2023", + language = "en" +} + +@ARTICLE{Hao2021-qp, + title = "Integrated analysis of multimodal single-cell data", + author = "Hao, Yuhan and Hao, Stephanie and Andersen-Nissen, Erica and + Mauck, 3rd, William M and Zheng, Shiwei and Butler, Andrew and + Lee, Maddie J and Wilk, Aaron J and Darby, Charlotte and Zager, + Michael and Hoffman, Paul and Stoeckius, Marlon and Papalexi, + Efthymia and Mimitou, Eleni P and Jain, Jaison and Srivastava, + Avi and Stuart, Tim and Fleming, Lamar M and Yeung, Bertrand and + Rogers, Angela J and McElrath, Juliana M and Blish, Catherine A + and Gottardo, Raphael and Smibert, Peter and Satija, Rahul", + abstract = "The simultaneous measurement of multiple modalities represents an + exciting frontier for single-cell genomics and necessitates + computational methods that can define cellular states based on + multimodal data. Here, we introduce ``weighted-nearest neighbor'' + analysis, an unsupervised framework to learn the relative utility + of each data type in each cell, enabling an integrative analysis + of multiple modalities. We apply our procedure to a CITE-seq + dataset of 211,000 human peripheral blood mononuclear cells + (PBMCs) with panels extending to 228 antibodies to construct a + multimodal reference atlas of the circulating immune system. + Multimodal analysis substantially improves our ability to resolve + cell states, allowing us to identify and validate previously + unreported lymphoid subpopulations. Moreover, we demonstrate how + to leverage this reference to rapidly map new datasets and to + interpret immune responses to vaccination and coronavirus disease + 2019 (COVID-19). Our approach represents a broadly applicable + strategy to analyze single-cell multimodal datasets and to look + beyond the transcriptome toward a unified and multimodal + definition of cellular identity.", + journal = "Cell", + volume = 184, + number = 13, + pages = "3573--3587.e29", + month = jun, + year = 2021, + keywords = "CITE-seq; COVID-19; T cell; immune system; multimodal analysis; + reference mapping; single cell + genomics;Thesis/prostatrend/03\_Methods;Thesis/prostatrend/05\_PCa\_Atlas;CAR + Tcell + Atlas;Grieb\_et\_al\_2023;MAVO-MCF-PUB/00\_thesis/PCa\_Atlas;ProstaTrend\_rev\_PUB/03\_Methods", + language = "en" +} + +@ARTICLE{Andreatta2021-fw, + title = "Interpretation of {T} cell states from single-cell + transcriptomics data using reference atlases", + author = "Andreatta, Massimo and Corria-Osorio, Jesus and M{\"u}ller, + S{\"o}ren and Cubas, Rafael and Coukos, George and Carmona, + Santiago J", + abstract = "Single-cell RNA sequencing (scRNA-seq) has revealed an + unprecedented degree of immune cell diversity. However, + consistent definition of cell subtypes and cell states across + studies and diseases remains a major challenge. Here we generate + reference T cell atlases for cancer and viral infection by + multi-study integration, and develop ProjecTILs, an algorithm for + reference atlas projection. In contrast to other methods, + ProjecTILs allows not only accurate embedding of new scRNA-seq + data into a reference without altering its structure, but also + characterizing previously unknown cell states that ``deviate'' + from the reference. ProjecTILs accurately predicts the effects of + cell perturbations and identifies gene programs that are altered + in different conditions and tissues. A meta-analysis of + tumor-infiltrating T cells from several cohorts reveals a strong + conservation of T cell subtypes between human and mouse, + providing a consistent basis to describe T cell heterogeneity + across studies, diseases, and species.", + journal = "Nat. Commun.", + volume = 12, + number = 1, + pages = "2965", + month = may, + year = 2021, + keywords = "Reference Atlas;Grieb\_et\_al\_2023", + language = "en" +} + +@ARTICLE{Korsunsky2019-hj, + title = "Fast, sensitive and accurate integration of single-cell data with + Harmony", + author = "Korsunsky, Ilya and Millard, Nghia and Fan, Jean and Slowikowski, + Kamil and Zhang, Fan and Wei, Kevin and Baglaenko, Yuriy and + Brenner, Michael and Loh, Po-Ru and Raychaudhuri, Soumya", + abstract = "The emerging diversity of single-cell RNA-seq datasets allows for + the full transcriptional characterization of cell types across a + wide variety of biological and clinical conditions. However, it + is challenging to analyze them together, particularly when + datasets are assayed with different technologies, because + biological and technical differences are interspersed. We present + Harmony (https://github.com/immunogenomics/harmony), an algorithm + that projects cells into a shared embedding in which cells group + by cell type rather than dataset-specific conditions. Harmony + simultaneously accounts for multiple experimental and biological + factors. In six analyses, we demonstrate the superior performance + of Harmony to previously published algorithms while requiring + fewer computational resources. Harmony enables the integration of + ~106 cells on a personal computer. We apply Harmony to peripheral + blood mononuclear cells from datasets with large experimental + differences, five studies of pancreatic islet cells, mouse + embryogenesis datasets and the integration of scRNA-seq with + spatial transcriptomics data.", + journal = "Nat. Methods", + volume = 16, + number = 12, + pages = "1289--1296", + month = dec, + year = 2019, + keywords = " + Thesis/prostatrend/03\_Methods;Thesis/mcf/03\_Methods/scRNA-Seq;Thesis/prostatrend/05\_PCa\_Atlas;CAR + Tcell + Atlas;Grieb\_et\_al\_2023;MAVO-MCF-PUB/00\_thesis/PCa\_Atlas;MAVO-MCF-PUB/03\_Methods/scRNA-Seq;ProstaTrend\_rev\_PUB/03\_Methods", + language = "en" +} + +@ARTICLE{Tirosh2016-ct, + title = "Dissecting the multicellular ecosystem of metastatic melanoma by + single-cell {RNA-seq}", + author = "Tirosh, Itay and Izar, Benjamin and Prakadan, Sanjay M and + Wadsworth, 2nd, Marc H and Treacy, Daniel and Trombetta, John J + and Rotem, Asaf and Rodman, Christopher and Lian, Christine and + Murphy, George and Fallahi-Sichani, Mohammad and Dutton-Regester, + Ken and Lin, Jia-Ren and Cohen, Ofir and Shah, Parin and Lu, + Diana and Genshaft, Alex S and Hughes, Travis K and Ziegler, + Carly G K and Kazer, Samuel W and Gaillard, Aleth and Kolb, + Kellie E and Villani, Alexandra-Chlo{\'e} and Johannessen, Cory M + and Andreev, Aleksandr Y and Van Allen, Eliezer M and + Bertagnolli, Monica and Sorger, Peter K and Sullivan, Ryan J and + Flaherty, Keith T and Frederick, Dennie T and Jan{\'e}-Valbuena, + Judit and Yoon, Charles H and Rozenblatt-Rosen, Orit and Shalek, + Alex K and Regev, Aviv and Garraway, Levi A", + abstract = "To explore the distinct genotypic and phenotypic states of + melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) + to 4645 single cells isolated from 19 patients, profiling + malignant, immune, stromal, and endothelial cells. Malignant + cells within the same tumor displayed transcriptional + heterogeneity associated with the cell cycle, spatial context, + and a drug-resistance program. In particular, all tumors harbored + malignant cells from two distinct transcriptional cell states, + such that tumors characterized by high levels of the MITF + transcription factor also contained cells with low MITF and + elevated levels of the AXL kinase. Single-cell analyses suggested + distinct tumor microenvironmental patterns, including + cell-to-cell interactions. Analysis of tumor-infiltrating T cells + revealed exhaustion programs, their connection to T cell + activation and clonal expansion, and their variability across + patients. Overall, we begin to unravel the cellular ecosystem of + tumors and how single-cell genomics offers insights with + implications for both targeted and immune therapies.", + journal = "Science", + volume = 352, + number = 6282, + pages = "189--196", + month = apr, + year = 2016, + keywords = "Thesis/prostatrend/03\_Methods;Thesis/prostatrend/05\_PCa\_Atlas;Grieb\_et\_al\_2023;UCCL\_Melanom\_Kunz\_SpatialSeq;MAVO-MCF-PUB/00\_thesis/PCa\_Atlas;ProstaTrend\_rev\_PUB/03\_Methods", + language = "en" +} + +@ARTICLE{Azizi2018-cg, + title = "{Single-Cell} Map of Diverse Immune Phenotypes in the Breast + Tumor Microenvironment", + author = "Azizi, Elham and Carr, Ambrose J and Plitas, George and Cornish, + Andrew E and Konopacki, Catherine and Prabhakaran, Sandhya and + Nainys, Juozas and Wu, Kenmin and Kiseliovas, Vaidotas and Setty, + Manu and Choi, Kristy and Fromme, Rachel M and Dao, Phuong and + McKenney, Peter T and Wasti, Ruby C and Kadaveru, Krishna and + Mazutis, Linas and Rudensky, Alexander Y and Pe'er, Dana", + abstract = "Knowledge of immune cell phenotypes in the tumor microenvironment + is essential for understanding mechanisms of cancer progression + and immunotherapy response. We profiled 45,000 immune cells from + eight breast carcinomas, as well as matched normal breast tissue, + blood, and lymph nodes, using single-cell RNA-seq. We developed a + preprocessing pipeline, SEQC, and a Bayesian clustering and + normalization method, Biscuit, to address computational + challenges inherent to single-cell data. Despite significant + similarity between normal and tumor tissue-resident immune cells, + we observed continuous phenotypic expansions specific to the + tumor microenvironment. Analysis of paired single-cell RNA and T + cell receptor (TCR) sequencing data from 27,000 additional T + cells revealed the combinatorial impact of TCR utilization on + phenotypic diversity. Our results support a model of continuous + activation in T cells and do not comport with the macrophage + polarization model in cancer. Our results have important + implications for characterizing tumor-infiltrating immune cells.", + journal = "Cell", + volume = 174, + number = 5, + pages = "1293--1308.e36", + month = aug, + year = 2018, + keywords = "Bayesian modeling; T cell activation; TCR utilization; breast + cancer; single-cell RNA-seq; tumor microenvironment; + tumor-infiltrating immune + cells;Immune-Phenotypes;T-cell;scRNA-Seq;scRNA-Seq;Thesis/prostatrend/03\_Methods;Thesis/mcf/03\_Methods/scRNA-Seq;Thesis/prostatrend/05\_PCa\_Atlas;CAR + Tcell + Atlas;Grieb\_et\_al\_2023;MAVO-MCF-PUB/00\_thesis/PCa\_Atlas;MAVO-MCF-PUB/03\_Methods/scRNA-Seq;ProstaTrend\_rev\_PUB/03\_Methods", + language = "en" +} diff --git a/code/supplementary_file/supplementary_information-blx.bib b/code/supplementary_file/supplementary_information-blx.bib new file mode 100644 index 0000000..6ecc365 --- /dev/null +++ b/code/supplementary_file/supplementary_information-blx.bib @@ -0,0 +1,11 @@ +@Comment{$ biblatex control file $} +@Comment{$ biblatex bcf format version 3.7 $} +% Do not modify this file! +% +% This is an auxiliary file used by the 'biblatex' package. +% This file may safely be deleted. It will be recreated as +% required. + +@Control{biblatex-control, + options = {3.7:0:0:1:0:1:1:0:0:0:0:0:99:1:99:1:0:0:3:1:79:+:+:none}, +} diff --git a/code/supplementary_file/supplementary_information.tex b/code/supplementary_file/supplementary_information.tex new file mode 100644 index 0000000..c627903 --- /dev/null +++ b/code/supplementary_file/supplementary_information.tex @@ -0,0 +1,363 @@ +\documentclass[10pt]{paper} +\usepackage[bottom=1in,top=1in, left=1in, right=1in]{geometry} +\usepackage[utf8]{inputenc} +\usepackage{breakurl} +\usepackage{xspace} +\usepackage{float} +\usepackage[export]{adjustbox} +\usepackage{capt-of} +\usepackage{booktabs} +\usepackage{rotating} +\usepackage[colorinlistoftodos]{todonotes} +\usepackage{xcolor} +\usepackage{tabularx} +\usepackage{longtable} +\usepackage{makecell} +\usepackage{blindtext} +\usepackage{caption} +\usepackage{csquotes} +\usepackage[htt]{hyphenat} +\usepackage[ngerman,english]{babel} +\usepackage{colortbl} % To color rows in the tables + +\renewcommand\_{\textunderscore\allowbreak} + +\definecolor{mybluei}{RGB}{82,102,145} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Shortcuts +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\newcommand{\limma}{\texttt{limma}} +\newcommand{\Seurat}{\texttt{Seurat}} +\newcommand{\R}{\texttt{R}} +\newcommand{\Bioconductor}{\texttt{Bioconductor}} + +\newcommand{\mr}[1]{\begingroup\color{blue}\textbf{Michael: }#1\endgroup} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Images/captions +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage{graphicx} +\graphicspath{% + {../figures/main/} + {../figures/supplement/} +} + +% Add suffix S to figures and tables +\setcounter{table}{0} +\renewcommand{\thetable}{S\arabic{table}} + +\setcounter{figure}{0} +\renewcommand{\thefigure}{S\arabic{figure}} + +% Font size for captions +\captionsetup{format=hang} +\captionsetup[figure]{font=small, labelfont=bf, textfont=onehalfspacing, belowskip=5pt} +\captionsetup[table]{font=small, labelfont=bf, textfont=onehalfspacing} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Bibliography +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage[% + autocite = plain, + backend = bibtex, + doi = true, + url = true, + giveninits = true, + hyperref = true, + maxbibnames = 99, + maxcitenames = 99, + sortcites = true, + style = numeric, + sorting = none, + ]{biblatex} + +\input{bibliography-mimosis} +\addbibresource{references.bib} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Fonts +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage[T1]{fontenc} +\usepackage{charter} +\usepackage[expert]{mathdesign} + +\usepackage[ + activate={true,nocompatibility}, + final,tracking=true, + kerning=true, + spacing=true, + factor=1100, + stretch=10, + shrink=10]{microtype} +\microtypecontext{spacing=nonfrench} +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Hyperlinks +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\definecolor{myblue}{RGB}{82,102,145} + +\usepackage[ + colorlinks = true, + citecolor = myblue, + linkcolor = myblue, + urlcolor = myblue, + unicode, + ]{hyperref} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Spacing +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\RequirePackage{setspace} +\onehalfspacing +\usepackage{parskip} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Proper typesetting of units +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\RequirePackage{siunitx} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Mathematics +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\RequirePackage{amsmath} +\RequirePackage{amsthm} +\RequirePackage{dsfont} +\RequirePackage{textcomp} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% TOC, sections +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage{tocloft} +\setcounter{tocdepth}{3} % remove subsubsection from TOC + +\renewcommand{\cftpartfont} + {\usefont{T1}{qhv}{b}{n}\Large} +\renewcommand{\cftsecfont} + {\usefont{T1}{bch}{b}{n}\selectfont} +\renewcommand{\cftsubsecfont} + {\usefont{T1}{bch}{m}{n}\selectfont} + +\usepackage{titlesec} +\titleformat*{\subsubsection}{\usefont{T1}{qhv}{b}{n}\selectfont\color{mybluei}} +\titleformat*{\subsection}{\usefont{T1}{qhv}{b}{n}\selectfont\color{mybluei}} +\titleformat{\section}[hang]{ + \color{mybluei}\usefont{T1}{qhv}{b}{n}\selectfont} % "qhv" - TeX Gyre Heros, "b" - bold + {} + {0em} + {\hspace{-0.4pt}\Large \thesection\hspace{0.6em}} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Penalties +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\clubpenalty = 10000 +\widowpenalty = 10000 +\displaywidowpenalty = 10000 + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% START; first page +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\usepackage[most]{tcolorbox} + +\newcommand{\mybox}[4]{ + \begin{figure}[h] + \centering + \begin{tikzpicture} + \node[anchor=text,text width=\columnwidth-1.2cm, draw, rounded corners, line width=1pt, fill=#3, inner sep=5mm] (big) {\\#4}; + \node[draw, rounded corners, line width=.5pt, fill=#2, anchor=west, xshift=5mm] (small) at (big.north west) {#1}; + \end{tikzpicture} + \end{figure} +} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Title, TOC, list of figures and tables +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\title{\vspace{7cm}Supplementary information \\ \textnormal{Single cell multi-omic dissection of response and resistance to chimeric antigen receptor T cells against BCMA in relapsed multiple myeloma}} +% \author{...} + +\begin{document} + +\maketitle + +% \renewcommand*\contentsname{\vspace*{-45pt}} +% +% { +% \microtypesetup{protrusion=false} +% \hypersetup{linkcolor=myblue} +% \tableofcontents +% \microtypesetup{protrusion=true} +% } + +% \listoffigures +\thispagestyle{empty} + +\clearpage + +% \listoftables +% \thispagestyle{empty} + +% \clearpage + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Main part +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% + +\section{Annotation of T cell subtypes} + +Although we annotated the T cells using PBMC and BMMC reference atlases with Seurat's multimodal mapping method (see methods in main part), T cells were re-annotated using the ProjecTILs framework \cite{Andreatta2021-fw}. The authors of ProjecTILs provide T cell references atlases and a comprehensive workflow (see \href{https://github.com/carmonalab}{\nolinkurl{https://github.com/carmonalab}}) to annotate T-cell subtypes at a higher resolution compared to PBMC/BMMC references. + +\subsection{Purifying T cell from single-cell datasets} \label{cell_filer_2} + +To filter for T cells, we used the \R\ package \texttt{scGate} \cite{Andreatta2022-al}, which requires as input (i) a normalized gene expression matrix or Seurat object and (ii) a "gating model" (GM) consisting of gene sets representing the cell population of interest. The GM for T cells was retrieved using \texttt{scGate::get\_scGateDB()}. The enrichment of the gene sets in each cell was estimated using the rank-based method UCell \cite{Andreatta2021-yh}. The scGate method then performs smoothing of the enrichment scores with the k-Nearest-Neighbour (kNN) algorithm by calculating the mean UCell score across neighbouring cells. Finally, a fixed threshold is applied to the kNN-smoothed enrichment scores using binary decision trees derived from the GM. T cell purification was performed for each sample. + +\subsubsection{T cell subtype classification and annotation} \label{cell_filer_3} + +To classify CD4 and CD8 T cell identities, we projected CD4 and CD8 T cells separately into two reference maps. As reference, we used the single-cell atlases for CD4 and CD8 T cells, which are part of the ProjecTILs \cite{Andreatta2021-fw} framework (CD8: \href{https://figshare.com/ndownloader/files/38921366}{\nolinkurl{https://figshare.com/ndownloader/files/38921366}}; CD4: \href{https://figshare.com/ndownloader/files/39012395}{\nolinkurl{https://figshare.com/ndownloader/files/39012395}}). Prior to classification, the T cells (see \ref{cell_filer_2}) were divided into CD4 and CD8 using the \texttt{scGate} method. The corresponding gating models were part of the downloaded T cell atlases. Running the \texttt{ProjecTILs.classifier()} function for each sample on the two maps allowed combining the identity predictions for a complete annotation of the T cell identities. Only cells that can be unequivocally assigned to one cell identity are labeled; the remaining cells (including those that receive a label by more than one reference) are assigned to "Not Estimable". + +In addition, we annotated CD4 and CD8 T cells lineages based on the gene expression data using following strategy: Given the rawcounts, one cell was considered as CD8 positive or negative if the count value of CD8A or CD8B was $>$0 or $\leq$0, respectively. One cell was considered as CD4 positive or negative if the count value of CD4 was $>$0 or $\leq$0, respectively. This resulted in a classification of CD8-CD4+, CD8+CD4-, CD8+CD4+ and CD8-CD4- T cells. Cells that were annotated as CD8+CD4+ and whose cell identities were annotated as CD4 or CD8 by the ProjecTILs framekwork were labeled as "Not Estimable". We are aware that this is a rather conservative step. However, it allows us to avoid false positive annotations. Cells annotated as CD8+CD4- whose cell identities were annotated as CD4 by the ProjecTILs framework were also annotated as "Not Estimable" (and vice versa). + +To identify proliferating cell cluster, we applied the \texttt{run\_gsea} function implemented in the \texttt{clustifyr} \R\ package \cite{Fu2020-fp} using previously published G2/M and S-phase gene sets \cite{Tirosh2016-ct} as queries. As suggested by the authors, an overclustering was conducted. For this purpose, the cluster resolution was set to 1 (see \ref{integration_analysis}). The number of permutations was set to 1000. One cluster was significantly (p-value <0.1) enriched with cell cycle genes from the G2/M and S phases and was annotated as cyclic accordingly. + + + +\clearpage + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% \begin{figure}[ht!] +% \includegraphics[width=16.5cm, center]{supp_figure_pbmc_post_vs_pre.pdf} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Comparing post- and pre-infusional BMMCs and PBMCs in nonCR and CR] +% {\textbf{Comparing post- and pre-infusional BMMCs and PBMCs in nonCR and CR.} \textbf{(a)} Comparison between post- and pre-infusional cell types in responders (CR) and non-responders (nonCR). \textbf{(b)} Differential gene expression analysis comparing post- with pre-infusional BMMCs and PBMCs in CR and nonCR. \textbf{(c)} GO term enrichment analysis of significantly differentially expressed genes (post- vs. pre-infusional). Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the top 10 significantly enriched GO terms for biological processes (FDR <0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{post_PBMC_nonCR_vs_CR_ora.pdf} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Enrichment analysis for post-infusional BMMCs and PBMCs] +% {\textbf{Enrichment analysis for post-infusional BMMCs and PBMCs} GO term enrichment analysis of significantly differentially expressed genes (non-responders vs. responders) for BMMCs and PBMCs. Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the top 10 significantly enriched GO terms for biological processes (FDR <0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. +% } +% \label{fig:ora_pre_noncr_vs_cr} +% \end{figure} + +\begin{figure}[ht!] + \includegraphics[width=16cm, center]{tcell_ident_marker.pdf} + \captionsetup{format=plain} + \caption[Marker genes for T cell identities] + {\textbf{Marker genes for T cell identities.} DE genes for each T cell identity were determined using the Wilcoxon rank sum test. Only CAR negative cells were analyzed. Genes with an FDR <0.05 and an absolute fold change >1.5 were considered statistically significant. Genes are ranked according to FDR. For each cell identity, 5 significant genes are shown. The colour intensity indicates the standardized average expression level in a cell identity. The dot size indicates the percentage of expressing cells within each cell identity of the corresponding genes. + } + \label{fig:tcell_marker} +\end{figure} + +% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% % Monocle +% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p12_cd4.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD4+ T cell trajectories before/after Ide-cel (P12)] +% {\textbf{Single CD4+ T cell trajectories before/after Ide-cel (P12).} The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p12_cd8.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD8+ T cell trajectories before/after Ide-cel (P12)] +% {\textbf{Single CD8+ T cell trajectories before/after Ide-cel (P12).}The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p14_cd4.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD4+ T cell trajectories before/after Ide-cel (P14)] +% {\textbf{Single CD4+ T cell trajectories before/after Ide-cel (P14).} The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p14_cd8.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD8+ T cell trajectories before/after Ide-cel (P14)] +% {\textbf{Single CD8+ T cell trajectories before/after Ide-cel (P14).}The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p7_cd4.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD4+ T cell trajectories before/after Ide-cel (P07)] +% {\textbf{Single CD4+ T cell trajectories before/after Ide-cel (P07).} The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p7_cd8.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD8+ T cell trajectories before/after Ide-cel (P07)] +% {\textbf{Single CD8+ T cell trajectories before/after Ide-cel (P07).}The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p8_cd4.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD4+ T cell trajectories before/after Ide-cel (P08)] +% {\textbf{Single CD4+ T cell trajectories before/after Ide-cel (P08).} The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{monocle_p8_cd8.png} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Single CD8+ T cell trajectories before/after Ide-cel (P08)] +% {\textbf{Single CD8+ T cell trajectories before/after Ide-cel (P08).}The label indicates the root in the principal graph. \% Max = Expression is scaled to percent of the maximum expression. +% } +% \label{fig:post_vs_pre} +% \end{figure} + +% \begin{figure}[ht!] +% \includegraphics[width=16.5cm, center]{tcell_comp_post_vs_pre.pdf} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Comparing post- with pre-infusional T cell types in nonCR and CR] +% {\textbf{Comparing post- with pre-infusional T cell types in nonCR and CR.} \textbf{(a)} Differences in T cell type composition between post and post- and pre-infusional PBMCs from CR and nonCR. For each cell type, the log fold change in mean cell fraction between post- and pre-infusional samples was calculated with the R package \texttt{speckle}. The cell fraction calculation includes all cell types (the denominator is the sum of all cells analyzed). For clarity, only cell types with a fold change >1.5 are shown.\textbf{(b)} GO term enrichment analysis for DEGs comparing post- with pre-infusional PBMCs in CR and nonCR. GO terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the top 10 significantly enriched GO terms for biological processes (FDR <0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. +% } +% \label{fig:post_vs_pre} +% \end{figure} +% +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{CAR_vs_pre_T_ora.pdf} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Enrichment analysis in CAR T cells (CD4 and CD8) compared to pre-infusional T cells from patients treated with Ide-cel] +% {\textbf{Enrichment analysis in CAR T cells (CD4 and CD8) compared to pre-infusional T cells from patients treated with Ide-cel} GO term enrichment analysis of significantly differentially expressed genes (CAR vs. pre-infusional T cells). Terms are ranked by rich factor, which is the number of DE genes in the term divided by the number of background genes in that term. Dot plot depict the top 10 significantly enriched GO terms for biological processes (FDR <0.05). The dot size indicates the z-score, which is the number of DE genes with logFC >0 minus the number of DE genes with logFC <0 divided by the square root of the number of term-associated genes. Grey/white dots indicate the same number of genes with a logFC >0 and <0. +% } +% \label{fig:ora_pre_noncr_vs_cr} +% \end{figure} +% +% \begin{figure}[ht!] +% \includegraphics[width=16cm, center]{tcell_dgea_p12_CAR_vs_pre.pdf} +% \captionsetup{format=plain, font=footnotesize} +% \caption[Enrichment analysis in CAR T cells (T cell subtypes) compared to pre-infusional T cells from patients treated with Ide-cel] +% {\textbf{Enrichment analysis in CAR T cells (T cell subtypes) compared to pre-infusional T cells from patients treated with Ide-cel} GO term enrichment analysis of significantly differentially expressed genes (CAR vs. pre-infusional T cells).No significantly enriched GO terms were found for patient 14. +% } +% \label{fig:ora_pre_noncr_vs_cr} +% \end{figure} + +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +% Bibliography +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\clearpage +\printbibliography[heading=bibintoc] + +\end{document} diff --git a/code/tables/README b/code/tables/README new file mode 100644 index 0000000..e69de29 diff --git a/data/clinicopathological_discovery.csv b/data/clinicopathological_discovery.csv new file mode 100644 index 0000000..b5245d9 --- /dev/null +++ b/data/clinicopathological_discovery.csv @@ -0,0 +1,26 @@ +name,days.from.apharesis,days.from.infusion,date of birth,collection day,T-cell apharesis,CAR Infusion,sex,Age,barcode,source,remission after CAR,SAMPLE_NAME,ID +Patient 001,79,30,20/2/64,19/8/22,1/6/22,20/7/22,m,59,UC3BCAMC01,BM,PD,MXMERZ002A_01,P01_79_30_BM +Patient 001,79,30,20/2/64,19/8/22,1/6/22,20/7/22,m,59,UC3BCAPB01,PB,PD,MXMERZ002A_02,P01_79_30_PB +Patient 007,-31,-114,22/12/49,16/8/21,16/9/21,8/12/21,m,73,UC12HHPB01,PB,CR,MXMERZ002A_03,P07_-31_-114_PB +Patient 007,110,27,22/12/49,4/1/22,16/9/21,8/12/21,m,73,UC1QT6MC01,BM,CR,MXMERZ002A_04,P07_110_27_BM +Patient 011,0,-76,28/12/67,13/4/22,13/4/22,28/6/22,m,55,UC2JBNPB01,PB,PR,MXMERZ002A_05,P11_0_-76_PB 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009,1,-57,27/5/61,1/2/22,31/1/22,30/3/22,f,61,UC1RB1PB01,PB,CR,MXMERZ002A_15,P09_1_-57_PB +Patient 012,119,63,23/5/68,31/8/22,4/5/22,29/6/22,f,54,UC31NUMC01,BM,CR,MXMERZ002A_16,P12_119_63_BM +Patient 013,0,-54,3/6/64,5/5/22,5/5/22,28/6/22,m,58,UC2QESPB01,PB,PR,MXMERZ002A_17,P13_0_-54_PB +Patient 014,85,30,16/4/83,12/8/22,19/5/22,13/7/22,f,39,UC2YFTPB01,PB,CR,MXMERZ002A_18,P14_85_30_PB +Patient 008,-6,-82,24/9/59,3/9/21,9/9/21,24/11/21,m,63,UC1QQCPB01,PB,CR,MXMERZ002A_19,P08_-6_-82_PB +Patient 008,103,27,24/9/59,21/12/21,9/9/21,24/11/21,m,63,UC1RVXPB01,PB,CR,MXMERZ002A_20,P08_103_27_PB +Patient 009,102,44,27/5/61,13/5/22,31/1/22,30/3/22,f,61,UC2RGKMC01,BM,CR,MXMERZ002A_21,P09_102_44_BM +Patient 010,0,-54,7/6/79,6/4/22,6/4/22,30/5/22,m,43,UC2JAPPB01,PB,PD,MXMERZ002A_22,P10_0_-54_PB +Patient 010,133,79,7/6/79,17/8/22,6/4/22,30/5/22,m,43,UC31PQPB01,PB,PD,MXMERZ002A_23,P10_133_79_PB +Patient 012,-1,-57,23/5/68,3/5/22,4/5/22,29/6/22,f,54,UC2QQ4PB01,PB,CR,MXMERZ002A_24,P12_-1_-57_PB +Patient 013,84,30,3/6/64,28/7/22,5/5/22,28/6/22,m,58,UC32PKPB01,PB,PR,MXMERZ002A_25,P13_84_30_PB \ No newline at end of file diff --git a/data/clinicopathological_discovery.xlsx b/data/clinicopathological_discovery.xlsx new file mode 100644 index 0000000..c9fc03a Binary files /dev/null and b/data/clinicopathological_discovery.xlsx differ diff --git a/data/cytoBand.txt b/data/cytoBand.txt new file mode 100755 index 0000000..66dbeba --- /dev/null +++ b/data/cytoBand.txt @@ -0,0 +1,1478 @@ +chr1 0 2300000 p36.33 gneg +chr1 2300000 5300000 p36.32 gpos25 +chr1 5300000 7100000 p36.31 gneg +chr1 7100000 9100000 p36.23 gpos25 +chr1 9100000 12500000 p36.22 gneg +chr1 12500000 15900000 p36.21 gpos50 +chr1 15900000 20100000 p36.13 gneg +chr1 20100000 23600000 p36.12 gpos25 +chr1 23600000 27600000 p36.11 gneg +chr1 27600000 29900000 p35.3 gpos25 +chr1 29900000 32300000 p35.2 gneg +chr1 32300000 34300000 p35.1 gpos25 +chr1 34300000 39600000 p34.3 gneg +chr1 39600000 43700000 p34.2 gpos25 +chr1 43700000 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98900000 q16.1 gpos100 +chr6 98900000 100000000 q16.2 gneg +chr6 100000000 105000000 q16.3 gpos100 +chr6 105000000 114200000 q21 gneg +chr6 114200000 117900000 q22.1 gpos75 +chr6 117900000 118100000 q22.2 gneg +chr6 118100000 125800000 q22.31 gpos100 +chr6 125800000 126800000 q22.32 gneg +chr6 126800000 130000000 q22.33 gpos75 +chr6 130000000 130900000 q23.1 gneg +chr6 130900000 134700000 q23.2 gpos50 +chr6 134700000 138300000 q23.3 gneg +chr6 138300000 142200000 q24.1 gpos75 +chr6 142200000 145100000 q24.2 gneg +chr6 145100000 148500000 q24.3 gpos75 +chr6 148500000 152100000 q25.1 gneg +chr6 152100000 155200000 q25.2 gpos50 +chr6 155200000 160600000 q25.3 gneg +chr6 160600000 164100000 q26 gpos50 +chr6 164100000 170805979 q27 gneg +chr6_GL000250v2_alt 0 4672374 gneg +chr6_GL000251v2_alt 0 4795265 gneg +chr6_GL000252v2_alt 0 4604811 gneg +chr6_GL000253v2_alt 0 4677643 gneg +chr6_GL000254v2_alt 0 4827813 gneg +chr6_GL000255v2_alt 0 4606388 gneg +chr6_GL000256v2_alt 0 4929269 gneg 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67500000 q11.21 gneg +chr7 67500000 72700000 q11.22 gpos50 +chr7 72700000 77900000 q11.23 gneg +chr7 77900000 86700000 q21.11 gpos100 +chr7 86700000 88500000 q21.12 gneg +chr7 88500000 91500000 q21.13 gpos75 +chr7 91500000 93300000 q21.2 gneg +chr7 93300000 98400000 q21.3 gpos75 +chr7 98400000 104200000 q22.1 gneg +chr7 104200000 104900000 q22.2 gpos50 +chr7 104900000 107800000 q22.3 gneg +chr7 107800000 115000000 q31.1 gpos75 +chr7 115000000 117700000 q31.2 gneg +chr7 117700000 121400000 q31.31 gpos75 +chr7 121400000 124100000 q31.32 gneg +chr7 124100000 127500000 q31.33 gpos75 +chr7 127500000 129600000 q32.1 gneg +chr7 129600000 130800000 q32.2 gpos25 +chr7 130800000 132900000 q32.3 gneg +chr7 132900000 138500000 q33 gpos50 +chr7 138500000 143400000 q34 gneg +chr7 143400000 148200000 q35 gpos75 +chr7 148200000 152800000 q36.1 gneg +chr7 152800000 155200000 q36.2 gpos25 +chr7 155200000 159345973 q36.3 gneg +chr7_GL383534v2_alt 0 119183 gneg +chr7_KI270803v1_alt 0 1111570 gneg 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69600000 72000000 q13.3 gneg +chr8 72000000 74600000 q21.11 gpos100 +chr8 74600000 74700000 q21.12 gneg +chr8 74700000 83500000 q21.13 gpos75 +chr8 83500000 85900000 q21.2 gneg +chr8 85900000 92300000 q21.3 gpos100 +chr8 92300000 97900000 q22.1 gneg +chr8 97900000 100500000 q22.2 gpos25 +chr8 100500000 105100000 q22.3 gneg +chr8 105100000 109500000 q23.1 gpos75 +chr8 109500000 111100000 q23.2 gneg +chr8 111100000 116700000 q23.3 gpos100 +chr8 116700000 118300000 q24.11 gneg +chr8 118300000 121500000 q24.12 gpos50 +chr8 121500000 126300000 q24.13 gneg +chr8 126300000 130400000 q24.21 gpos50 +chr8 130400000 135400000 q24.22 gneg +chr8 135400000 138900000 q24.23 gpos75 +chr8 138900000 145138636 q24.3 gneg +chr8_KI270810v1_alt 0 374415 gneg +chr8_KI270811v1_alt 0 292436 gneg +chr8_KI270812v1_alt 0 282736 gneg +chr8_KI270813v1_alt 0 300230 gneg +chr8_KI270814v1_alt 0 141812 gneg +chr8_KI270815v1_alt 0 132244 gneg +chr8_KI270816v1_alt 0 305841 gneg +chr8_KI270817v1_alt 0 158983 gneg 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q21.12 gneg +chr9 71300000 76600000 q21.13 gpos50 +chr9 76600000 78500000 q21.2 gneg +chr9 78500000 81500000 q21.31 gpos50 +chr9 81500000 84300000 q21.32 gneg +chr9 84300000 87800000 q21.33 gpos50 +chr9 87800000 89200000 q22.1 gneg +chr9 89200000 91200000 q22.2 gpos25 +chr9 91200000 93900000 q22.31 gneg +chr9 93900000 96500000 q22.32 gpos25 +chr9 96500000 99800000 q22.33 gneg +chr9 99800000 105400000 q31.1 gpos100 +chr9 105400000 108500000 q31.2 gneg +chr9 108500000 112100000 q31.3 gpos25 +chr9 112100000 114900000 q32 gneg +chr9 114900000 119800000 q33.1 gpos75 +chr9 119800000 123100000 q33.2 gneg +chr9 123100000 127500000 q33.3 gpos25 +chr9 127500000 130600000 q34.11 gneg +chr9 130600000 131100000 q34.12 gpos25 +chr9 131100000 133100000 q34.13 gneg +chr9 133100000 134500000 q34.2 gpos25 +chr9 134500000 138394717 q34.3 gneg +chr9_GL383539v1_alt 0 162988 gneg +chr9_GL383540v1_alt 0 71551 gneg +chr9_GL383541v1_alt 0 171286 gneg +chr9_GL383542v1_alt 0 60032 gneg +chr9_KI270717v1_random 0 40062 gneg +chr9_KI270718v1_random 0 38054 gneg +chr9_KI270719v1_random 0 176845 gneg +chr9_KI270720v1_random 0 39050 gneg +chr9_KI270823v1_alt 0 439082 gneg +chrM 0 16569 gneg +chrUn_GL000195v1 0 182896 gneg +chrUn_GL000213v1 0 164239 gneg +chrUn_GL000214v1 0 137718 gneg +chrUn_GL000216v2 0 176608 gneg +chrUn_GL000218v1 0 161147 gneg +chrUn_GL000219v1 0 179198 gneg +chrUn_GL000220v1 0 161802 gneg +chrUn_GL000224v1 0 179693 gneg +chrUn_GL000226v1 0 15008 gneg +chrUn_KI270302v1 0 2274 gneg +chrUn_KI270303v1 0 1942 gneg +chrUn_KI270304v1 0 2165 gneg +chrUn_KI270305v1 0 1472 gneg +chrUn_KI270310v1 0 1201 gneg +chrUn_KI270311v1 0 12399 gneg +chrUn_KI270312v1 0 998 gneg +chrUn_KI270315v1 0 2276 gneg +chrUn_KI270316v1 0 1444 gneg +chrUn_KI270317v1 0 37690 gneg +chrUn_KI270320v1 0 4416 gneg +chrUn_KI270322v1 0 21476 gneg +chrUn_KI270329v1 0 1040 gneg +chrUn_KI270330v1 0 1652 gneg +chrUn_KI270333v1 0 2699 gneg +chrUn_KI270334v1 0 1368 gneg +chrUn_KI270335v1 0 1048 gneg +chrUn_KI270336v1 0 1026 gneg +chrUn_KI270337v1 0 1121 gneg +chrUn_KI270338v1 0 1428 gneg +chrUn_KI270340v1 0 1428 gneg +chrUn_KI270362v1 0 3530 gneg +chrUn_KI270363v1 0 1803 gneg +chrUn_KI270364v1 0 2855 gneg +chrUn_KI270366v1 0 8320 gneg +chrUn_KI270371v1 0 2805 gneg +chrUn_KI270372v1 0 1650 gneg +chrUn_KI270373v1 0 1451 gneg +chrUn_KI270374v1 0 2656 gneg +chrUn_KI270375v1 0 2378 gneg +chrUn_KI270376v1 0 1136 gneg +chrUn_KI270378v1 0 1048 gneg +chrUn_KI270379v1 0 1045 gneg +chrUn_KI270381v1 0 1930 gneg +chrUn_KI270382v1 0 4215 gneg +chrUn_KI270383v1 0 1750 gneg +chrUn_KI270384v1 0 1658 gneg +chrUn_KI270385v1 0 990 gneg +chrUn_KI270386v1 0 1788 gneg +chrUn_KI270387v1 0 1537 gneg +chrUn_KI270388v1 0 1216 gneg +chrUn_KI270389v1 0 1298 gneg +chrUn_KI270390v1 0 2387 gneg +chrUn_KI270391v1 0 1484 gneg +chrUn_KI270392v1 0 971 gneg +chrUn_KI270393v1 0 1308 gneg +chrUn_KI270394v1 0 970 gneg +chrUn_KI270395v1 0 1143 gneg +chrUn_KI270396v1 0 1880 gneg +chrUn_KI270411v1 0 2646 gneg +chrUn_KI270412v1 0 1179 gneg 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94300000 99100000 q21.33 gpos75 +chrX 99100000 103300000 q22.1 gneg +chrX 103300000 104500000 q22.2 gpos50 +chrX 104500000 109400000 q22.3 gneg +chrX 109400000 117400000 q23 gpos75 +chrX 117400000 121800000 q24 gneg +chrX 121800000 129500000 q25 gpos100 +chrX 129500000 131300000 q26.1 gneg +chrX 131300000 134500000 q26.2 gpos25 +chrX 134500000 138900000 q26.3 gneg +chrX 138900000 141200000 q27.1 gpos75 +chrX 141200000 143000000 q27.2 gneg +chrX 143000000 148000000 q27.3 gpos100 +chrX 148000000 156040895 q28 gneg +chrX_KI270880v1_alt 0 284869 gneg +chrX_KI270881v1_alt 0 144206 gneg +chrX_KI270913v1_alt 0 274009 gneg +chrY 0 300000 p11.32 gneg +chrY 300000 600000 p11.31 gpos50 +chrY 600000 10300000 p11.2 gneg +chrY 10300000 10400000 p11.1 acen +chrY 10400000 10600000 q11.1 acen +chrY 10600000 12400000 q11.21 gneg +chrY 12400000 17100000 q11.221 gpos50 +chrY 17100000 19600000 q11.222 gneg +chrY 19600000 23800000 q11.223 gpos50 +chrY 23800000 26600000 q11.23 gneg +chrY 26600000 57227415 q12 gvar +chrY_KI270740v1_random 0 37240 gneg +chr8_KZ208915v1_fix 0 6367528 gneg +chr15_KN538374v1_fix 0 4998962 gneg +chr15_KQ031389v1_alt 0 2365364 gneg +chr16_KV880768v1_fix 0 1927115 gneg +chr12_KZ208916v1_fix 0 1046838 gneg +chr14_KZ208920v1_fix 0 690932 gneg +chr7_KZ208913v1_alt 0 680662 gneg +chr5_KV575244v1_fix 0 673059 gneg +chr7_KZ208912v1_fix 0 589656 gneg +chr1_KV880763v1_alt 0 551020 gneg +chr12_KN538369v1_fix 0 541038 gneg +chr2_KQ983256v1_alt 0 535088 gneg +chr2_KQ031384v1_fix 0 481245 gneg +chr7_KV880765v1_fix 0 468267 gneg +chr1_KQ031383v1_fix 0 467143 gneg +chr1_KN538360v1_fix 0 460100 gneg +chr3_KN196475v1_fix 0 451168 gneg +chr4_KV766193v1_alt 0 420675 gneg +chr10_KN538367v1_fix 0 420164 gneg +chr3_KN538364v1_fix 0 415308 gneg +chr3_KV766192v1_fix 0 411654 gneg +chr18_KQ090028v1_fix 0 407387 gneg +chr19_KQ458386v1_fix 0 405389 gneg +chr3_KQ031385v1_fix 0 373699 gneg +chr19_KN196484v1_fix 0 370917 gneg +chr2_KN538363v1_fix 0 365499 gneg +chr5_KV575243v1_alt 0 362221 gneg +chr13_KN538372v1_fix 0 356766 gneg +chr1_KQ458383v1_alt 0 349938 gneg +chr9_KN196479v1_fix 0 330164 gneg +chr1_KZ208906v1_fix 0 330031 gneg +chr6_KQ031387v1_fix 0 320750 gneg +chr12_KQ759760v1_fix 0 315610 gneg +chr3_KN196476v1_fix 0 305979 gneg +chr1_KN538361v1_fix 0 305542 gneg +chr19_KV575249v1_alt 0 293522 gneg +chr17_KV766196v1_fix 0 281919 gneg +chr1_KQ983255v1_alt 0 278659 gneg +chr10_KN196480v1_fix 0 277797 gneg +chr17_KV766198v1_alt 0 276292 gneg +chr6_KN196478v1_fix 0 268330 gneg +chr16_KQ090027v1_alt 0 267463 gneg +chr8_KV880767v1_fix 0 265876 gneg +chr10_KQ090021v1_fix 0 264545 gneg +chr17_KV766197v1_alt 0 246895 gneg +chr6_KQ090016v1_fix 0 245716 gneg +chr6_KZ208911v1_fix 0 242796 gneg +chr19_KV575250v1_alt 0 241058 gneg +chr4_KQ090015v1_alt 0 236512 gneg +chr4_KQ983257v1_fix 0 230434 gneg +chr19_KV575256v1_alt 0 223118 gneg +chr1_KQ458384v1_alt 0 212205 gneg +chr12_KN196482v1_fix 0 211377 gneg +chrY_KZ208924v1_fix 0 209722 gneg +chr2_KN538362v1_fix 0 208149 gneg +chr13_KN538371v1_fix 0 206320 gneg +chr4_KQ983258v1_alt 0 205407 gneg +chr18_KQ458385v1_alt 0 205101 gneg +chr11_KN538368v1_alt 0 203552 gneg +chr11_KQ759759v1_fix 0 196940 gneg +chrX_KV766199v1_alt 0 188004 gneg +chr1_KN196472v1_fix 0 186494 gneg +chr10_KQ090020v1_alt 0 185507 gneg +chr11_KQ090022v1_fix 0 181958 gneg +chr2_KZ208907v1_alt 0 181658 gneg +chr7_KQ031388v1_fix 0 179932 gneg +chr19_KV575252v1_alt 0 178197 gneg +chr3_KZ208909v1_alt 0 175849 gneg +chr12_KZ208918v1_alt 0 174808 gneg +chr22_KQ458388v1_alt 0 174749 gneg +chr14_KZ208919v1_alt 0 171798 gneg +chr19_KV575259v1_alt 0 171263 gneg +chr19_KV575247v1_alt 0 170206 gneg +chr16_KQ031390v1_alt 0 169136 gneg +chr13_KQ090024v1_alt 0 168146 gneg +chr19_KV575248v1_alt 0 168131 gneg +chr19_KV575253v1_alt 0 166713 gneg +chr1_KN196473v1_fix 0 166200 gneg +chr1_KZ208904v1_alt 0 166136 gneg +chr3_KQ031386v1_fix 0 165718 gneg +chr8_KZ208914v1_fix 0 165120 gneg +chr19_KV575246v1_alt 0 163926 gneg +chr9_KQ090018v1_alt 0 163882 gneg +chr4_KQ090014v1_alt 0 163749 gneg +chr19_KV575255v1_alt 0 161095 gneg +chr19_KV575251v1_alt 0 159285 gneg +chr8_KV880766v1_fix 0 156998 gneg +chr19_KV575258v1_alt 0 156965 gneg +chr22_KN196485v1_alt 0 156562 gneg +chr22_KQ458387v1_alt 0 155930 gneg +chr17_KV575245v1_fix 0 154723 gneg +chr22_KN196486v1_alt 0 153027 gneg +chr13_KN538373v1_fix 0 148762 gneg +chr19_KV575260v1_alt 0 145691 gneg +chr22_KQ759761v1_alt 0 145162 gneg +chr7_KV880764v1_fix 0 142129 gneg +chr1_KQ458382v1_alt 0 141019 gneg +chr11_KV766195v1_fix 0 140877 gneg +chr2_KZ208908v1_alt 0 140361 gneg +chr1_KZ208905v1_alt 0 140355 gneg +chr6_KV766194v1_fix 0 139427 gneg +chr5_KN196477v1_alt 0 139087 gneg +chr5_KZ208910v1_alt 0 135987 gneg +chr9_KQ090019v1_alt 0 134099 gneg +chr13_KQ090025v1_alt 0 123480 gneg +chr1_KN196474v1_fix 0 122022 gneg +chr12_KQ090023v1_alt 0 109323 gneg +chr11_KN196481v1_fix 0 108875 gneg +chrY_KN196487v1_fix 0 101150 gneg +chr22_KQ759762v1_fix 0 101037 gneg +chr19_KV575257v1_alt 0 100553 gneg +chr19_KV575254v1_alt 0 99845 gneg +chr18_KZ208922v1_fix 0 93070 gneg +chr4_KQ090013v1_alt 0 90922 gneg +chr12_KN538370v1_fix 0 86533 gneg +chr10_KN538366v1_fix 0 85284 gneg +chr6_KQ090017v1_alt 0 82315 gneg +chr16_KZ208921v1_alt 0 78609 gneg +chr12_KZ208917v1_fix 0 64689 gneg +chr16_KQ090026v1_alt 0 59016 gneg +chrY_KZ208923v1_fix 0 48370 gneg +chr13_KN196483v1_fix 0 35455 gneg +chr10_KN538365v1_fix 0 14347 gneg +chr16_KZ559113v1_fix 0 480415 gneg +chr11_KZ559108v1_fix 0 305244 gneg +chr3_KZ559103v1_alt 0 302885 gneg +chr11_KZ559110v1_alt 0 301637 gneg +chr11_KZ559109v1_fix 0 279644 gneg +chr18_KZ559115v1_fix 0 230843 gneg +chr3_KZ559102v1_alt 0 197752 gneg +chr3_KZ559105v1_alt 0 195063 gneg +chr11_KZ559111v1_alt 0 181167 gneg +chr7_KZ559106v1_alt 0 172555 gneg +chr3_KZ559101v1_alt 0 164041 gneg +chr18_KZ559116v1_alt 0 163186 gneg +chr12_KZ559112v1_alt 0 154139 gneg +chr17_KZ559114v1_alt 0 116753 gneg +chr3_KZ559104v1_fix 0 105527 gneg +chr8_KZ559107v1_alt 0 103072 gneg +chr1_KZ559100v1_fix 0 44955 gneg +chr15_ML143371v1_fix 0 5500449 gneg +chr21_ML143377v1_fix 0 519485 gneg +chr19_ML143376v1_fix 0 493165 gneg +chr22_ML143378v1_fix 0 461303 gneg +chr10_ML143354v1_fix 0 454963 gneg +chr22_ML143380v1_fix 0 412368 gneg +chr13_ML143366v1_fix 0 409912 gneg +chrX_ML143381v1_fix 0 403128 gneg +chr14_ML143367v1_fix 0 399183 gneg +chr15_ML143372v1_fix 0 396515 gneg +chr15_ML143370v1_fix 0 369264 gneg +chr4_ML143345v1_fix 0 341066 gneg +chr12_ML143361v1_fix 0 297568 gneg +chr10_ML143355v1_fix 0 292944 gneg +chr4_ML143349v1_fix 0 276109 gneg +chr16_ML143373v1_fix 0 270967 gneg +chr11_ML143358v1_fix 0 270122 gneg +chr14_ML143368v1_alt 0 264228 gneg +chr7_ML143352v1_fix 0 254759 gneg +chr4_ML143344v1_fix 0 235734 gneg +chr11_ML143359v1_fix 0 217075 gneg +chr3_ML143343v1_alt 0 215443 gneg +chr12_ML143362v1_fix 0 192531 gneg +chr4_ML143347v1_fix 0 176674 gneg +chr11_ML143360v1_fix 0 170928 gneg +chr11_ML143357v1_fix 0 165419 gneg +chr13_ML143364v1_fix 0 158944 gneg +chr2_ML143341v1_fix 0 145975 gneg +chr17_ML143374v1_fix 0 137908 gneg +chr4_ML143348v1_fix 0 125549 gneg +chr15_ML143369v1_fix 0 97763 gneg +chr5_ML143350v1_fix 0 89956 gneg +chr2_ML143342v1_fix 0 84043 gneg +chr6_ML143351v1_fix 0 73265 gneg +chrX_ML143383v1_fix 0 68192 gneg +chr13_ML143365v1_fix 0 65394 gneg +chr17_ML143375v1_fix 0 56695 gneg +chr4_ML143346v1_fix 0 53476 gneg +chr11_ML143356v1_fix 0 45257 gneg +chrX_ML143382v1_fix 0 28824 gneg +chr9_ML143353v1_fix 0 25408 gneg +chrX_ML143385v1_fix 0 17435 gneg +chrX_ML143384v1_fix 0 14678 gneg +chr22_ML143379v1_fix 0 12295 gneg +chr13_ML143363v1_fix 0 7309 gneg diff --git a/data/feature_reference.csv b/data/feature_reference.csv new file mode 100755 index 0000000..91c5618 --- /dev/null +++ b/data/feature_reference.csv @@ -0,0 +1,64 @@ +id,name,read,pattern,sequence,feature_type +CD3e,CD3E_proteona,R2,5PNNNNNNNNNN(BC),CTCATTGTAACTCCT,Antibody Capture +CD4,CD4_proteona,R2,5PNNNNNNNNNN(BC),TGTTCCCGCTCAACT,Antibody Capture +CD8a,CD8A_proteona,R2,5PNNNNNNNNNN(BC),GCTGCGCTTTCCATT,Antibody Capture +CD11b,CD11B_proteona,R2,5PNNNNNNNNNN(BC),GACAAGTGATCTGCA,Antibody Capture +CD11c,CD11C_proteona,R2,5PNNNNNNNNNN(BC),TACGCCTATAACTTG,Antibody Capture +CD14,CD14_proteona,R2,5PNNNNNNNNNN(BC),TCTCAGACCTCCGTA,Antibody Capture +CD15,CD15_proteona,R2,5PNNNNNNNNNN(BC),TCACCAGTACCTAGT,Antibody Capture +CD16,CD16_proteona,R2,5PNNNNNNNNNN(BC),AAGTTCACTCTTTGC,Antibody Capture +CD19,CD19_proteona,R2,5PNNNNNNNNNN(BC),CTGGGCAATTACTCG,Antibody Capture +CD25,CD25_proteona,R2,5PNNNNNNNNNN(BC),TTTGTCCTGTACGCC,Antibody Capture +CD28,CD28_proteona,R2,5PNNNNNNNNNN(BC),TGAGAACGACCCTAA,Antibody Capture +CD33,CD33_proteona,R2,5PNNNNNNNNNN(BC),TAACTCAGGGCCTAT,Antibody Capture +CD38,CD38_proteona,R2,5PNNNNNNNNNN(BC),CCTATTCCGATTCCG,Antibody Capture +CD39,CD39_proteona,R2,5PNNNNNNNNNN(BC),TTACCTGGTATCCGT,Antibody Capture +CD40,CD40_proteona,R2,5PNNNNNNNNNN(BC),CTCAGATGGAGTATG,Antibody Capture +CD45,CD45_proteona,R2,5PNNNNNNNNNN(BC),TGCAATTACCCGGAT,Antibody Capture +CD45RA,CD45RA_proteona,R2,5PNNNNNNNNNN(BC),TCAATCCTTCCGCTT,Antibody Capture +CD45RO,CD45RO_proteona,R2,5PNNNNNNNNNN(BC),CTCCGAATCATGTTG,Antibody Capture +CD56,CD56_proteona,R2,5PNNNNNNNNNN(BC),TCCTTTCCTGATAGG,Antibody Capture +CD57,CD57_proteona,R2,5PNNNNNNNNNN(BC),AACTCCCTATGGAGG,Antibody Capture +CD62L,CD62L_proteona,R2,5PNNNNNNNNNN(BC),GTCCCTGCAACTTGA,Antibody Capture +CD69,CD69_proteona,R2,5PNNNNNNNNNN(BC),GTCTCTTGGCTTAAA,Antibody Capture +CD80,CD80_proteona,R2,5PNNNNNNNNNN(BC),ACGAATCAATCTGTG,Antibody Capture +CD81,CD81_proteona,R2,5PNNNNNNNNNN(BC),GTATCCTTCCTTGGC,Antibody Capture +CD94,CD94_proteona,R2,5PNNNNNNNNNN(BC),CTTTCCGGTCCTACA,Antibody Capture +CD95,CD95_proteona,R2,5PNNNNNNNNNN(BC),CCAGCTCATTAGAGC,Antibody Capture +CD107a,CD107A_proteona,R2,5PNNNNNNNNNN(BC),CAGCCCACTGCAATA,Antibody Capture +CD117,CD117_proteona,R2,5PNNNNNNNNNN(BC),AGACTAATAGCTGAC,Antibody Capture +CD123,CD123_proteona,R2,5PNNNNNNNNNN(BC),CTTCACTCTGTCAGG,Antibody Capture +CD127,CD127_proteona,R2,5PNNNNNNNNNN(BC),GTGTGTTGTCCTATG,Antibody Capture +CD134,CD134_proteona,R2,5PNNNNNNNNNN(BC),AACCCACCGTTGTTA,Antibody Capture +CD137,CD137_proteona,R2,5PNNNNNNNNNN(BC),CAGTAAGTTCGGGAC,Antibody Capture +CD138,CD138_proteona,R2,5PNNNNNNNNNN(BC),ACTCTTTCGTTTACG,Antibody Capture +CD141,CD141_proteona,R2,5PNNNNNNNNNN(BC),GGATAACCGCGCTTT,Antibody Capture +CD152,CD152_proteona,R2,5PNNNNNNNNNN(BC),ATGGTTCACGTAATC,Antibody Capture +CD160,CD160_proteona,R2,5PNNNNNNNNNN(BC),GGCTAGAAATCAACG,Antibody Capture +CD161,CD161_proteona,R2,5PNNNNNNNNNN(BC),GTACGCAGTCCTTCT,Antibody Capture +CD183,CD183_proteona,R2,5PNNNNNNNNNN(BC),GCGATGGTAGATTAT,Antibody Capture +CD195,CD195_proteona,R2,5PNNNNNNNNNN(BC),CCAAAGTAAGAGCCA,Antibody Capture +CD197,CD197_proteona,R2,5PNNNNNNNNNN(BC),AGTTCAGTCAACCGA,Antibody Capture +CD200,CD200_proteona,R2,5PNNNNNNNNNN(BC),CACGTAGACCTTTGC,Antibody Capture +CD223,CD223_proteona,R2,5PNNNNNNNNNN(BC),CATTTGTCTGCCGGT,Antibody Capture +CD244,CD244_proteona,R2,5PNNNNNNNNNN(BC),TCGCTTGGATGGTAG,Antibody Capture +CD269,CD269_proteona,R2,5PNNNNNNNNNN(BC),CAGATGATCCACCAT,Antibody Capture +CD273,CD273_proteona,R2,5PNNNNNNNNNN(BC),TCAACGCTTGGCTAG,Antibody Capture +CD274,CD274_proteona,R2,5PNNNNNNNNNN(BC),GTTGTCCGACAATAC,Antibody Capture +CD278,CD278_proteona,R2,5PNNNNNNNNNN(BC),CGCGCACCCATTAAA,Antibody Capture +CD279,CD279_proteona,R2,5PNNNNNNNNNN(BC),ACAGCGCCGTATTTA,Antibody Capture +CD294,CD294_proteona,R2,5PNNNNNNNNNN(BC),TGTTTACGAGAGCCC,Antibody Capture +CD314,CD314_proteona,R2,5PNNNNNNNNNN(BC),CGTGTTTGTTCCTCA,Antibody Capture +CD319,CD319_proteona,R2,5PNNNNNNNNNN(BC),AGTATGCCATGTCTT,Antibody Capture +CD337,CD337_proteona,R2,5PNNNNNNNNNN(BC),AAAGTCACTCTGCCG,Antibody Capture +CD357,CD357_proteona,R2,5PNNNNNNNNNN(BC),ACCTTTCGACACTCG,Antibody Capture +CD366,CD366_proteona,R2,5PNNNNNNNNNN(BC),TGTCCTACCCAACTT,Antibody Capture +CD307,CD307_proteona,R2,5PNNNNNNNNNN(BC),TCACGCAGTCCTCAA,Antibody Capture +TCRAB,TCRAB_proteona,R2,5PNNNNNNNNNN(BC),CGTAACGTAGAGCGA,Antibody Capture +TCRV2,TCRV2_proteona,R2,5PNNNNNNNNNN(BC),TCAGTCAGATGGTAT,Antibody Capture +CCR10,CCR10_proteona,R2,5PNNNNNNNNNN(BC),ATCTGTATGTCACAG,Antibody Capture +HLA-DR,HLA-DR_proteona,R2,5PNNNNNNNNNN(BC),AATAGCGAGCAAGTA,Antibody Capture +KLRG1,KLRG1_proteona,R2,5PNNNNNNNNNN(BC),GTAGTAGGCTAGACC,Antibody Capture +TIGIT,TIGIT_proteona,R2,5PNNNNNNNNNN(BC),TTGCTTACCGCCAGA,Antibody Capture +FITC,FITC_proteona,R2,5PNNNNNNNNNN(BC),TTTGTGTTGTGGTAC,Antibody Capture +PE,PE_proteona,R2,5PNNNNNNNNNN(BC),TGACCAGTTCCGCAT,Antibody Capture diff --git a/data/gene_ordering.tsv b/data/gene_ordering.tsv new file mode 100644 index 0000000..4de120b --- /dev/null +++ b/data/gene_ordering.tsv @@ -0,0 +1,56051 @@ +DDX11L1 1 11869 14409 +WASH7P 1 14404 29570 +MIR6859-1 1 17369 17436 +RP11-34P13.3 1 29554 31109 +MIR1302-2 1 30366 30503 +FAM138A 1 34554 36081 +OR4G4P 1 52473 53312 +OR4G11P 1 57598 64116 +OR4F5 1 65419 71585 +RP11-34P13.7 1 89295 133723 +RP11-34P13.8 1 89551 91105 +RP11-34P13.10 1 131025 134836 +RP11-34P13.15 1 135141 135895 +RP11-34P13.16 1 137682 137965 +RP11-34P13.14 1 139790 140339 +RP11-34P13.13 1 141474 173862 +RNU6-1100P 1 157784 157887 +RP11-34P13.9 1 160446 161525 +ABC7-43046700E7.1 1 182696 184174 +RP11-34P13.18 1 185217 195411 +MIR6859-2 1 187891 187958 +AP006222.1 1 257864 359681 +RPL23AP24 1 347982 348366 +RP4-669L17.2 1 358857 366052 +RP4-669L17.4 1 365389 522928 +RP4-669L17.6 1 439870 440232 +OR4F29 1 450703 451697 +CICP7 1 487101 489906 +RP4-669L17.8 1 491225 493241 +RF00026 1 516376 516479 +RP11-206L10.17 1 586071 827796 +RP5-857K21.2 1 587629 594768 +RP5-857K21.9 1 629062 629433 +RP5-857K21.8 1 629640 630683 +RP5-857K21.6 1 631074 632616 +MIR6723 1 632325 632413 +RP5-857K21.7 1 632757 633438 +RP5-857K21.14 1 633535 633741 +RP5-857K21.10 1 633696 634376 +RP5-857K21.11 1 634376 634922 +RP5-857K21.12 1 674842 675265 +OR4F16 1 685679 686673 +RP11-206L10.15 1 722092 724903 +RP11-206L10.2 1 725885 778626 +RNU6-1199P 1 758233 758336 +RP11-206L10.4 1 760911 761989 +RP11-206L10.9 1 778770 810060 +RP11-206L10.18 1 781937 782050 +RP11-206L10.8 1 800879 817712 +FAM87B 1 817371 819837 +RP11-206L10.11 1 825138 859446 +NCRNA00115 1 826206 827522 +FAM41C 1 868071 876903 +TUBB8P11 1 873292 874349 +FAM166AP3 1 874529 877234 +RP11-54O7.16 1 904834 915976 +RP11-54O7.1 1 911435 914948 +RP11-54O7.2 1 914171 914971 +RP11-54O7.3 1 916865 921016 +SAMD11 1 923928 944581 +NOC2L 1 944204 959309 +KLHL17 1 960587 965715 +PLEKHN1 1 966497 975865 +PERM1 1 975204 982093 +RP11-54O7.17 1 995966 998051 +HES4 1 998962 1000172 +ISG15 1 1001138 1014541 +RP11-54O7.10 1 1008076 1008229 +RP11-54O7.11 1 1011997 1013193 +AGRN 1 1020123 1056118 +RP11-54O7.14 1 1055033 1056116 +RP11-465B22.3 1 1059734 1069355 +RP11-54O7.18 1 1062208 1063288 +RNF223 1 1070966 1074307 +C1orf159 1 1081818 1116361 +RP11-465B22.5 1 1137017 1144056 +MIR200B 1 1167104 1167198 +MIR200A 1 1167863 1167952 +MIR429 1 1169005 1169087 +RP11-465B22.8 1 1169357 1170343 +RP11-465B22.6 1 1173056 1179555 +TTLL10 1 1173884 1197935 +TNFRSF18 1 1203508 1206691 +TNFRSF4 1 1211326 1214138 +SDF4 1 1216908 1232031 +B3GALT6 1 1232265 1235041 +C1QTNF12 1 1242446 1246722 +RP5-902P8.12 1 1249777 1251334 +UBE2J2 1 1253909 1273885 +RP5-902P8.10 1 1275223 1280420 +SCNN1D 1 1280436 1292029 +ACAP3 1 1292376 1309609 +MIR6726 1 1296110 1296170 +PUSL1 1 1308567 1311677 +INTS11 1 1311585 1324691 +MIR6727 1 1312502 1312566 +RP5-890O3.9 1 1317581 1318689 +CPTP 1 1324756 1328897 +TAS1R3 1 1331314 1335306 +DVL1 1 1335276 1349350 +MIR6808 1 1339650 1339708 +MXRA8 1 1352689 1361777 +AURKAIP1 1 1373730 1375495 +RP5-890O3.3 1 1378666 1379032 +CCNL2 1 1385711 1399328 +RP4-758J18.2 1 1399522 1402046 +MRPL20 1 1401908 1407313 +RN7SL657P 1 1405460 1405752 +RP4-758J18.13 1 1409096 1410618 +ANKRD65 1 1418420 1421769 +RP4-758J18.7 1 1420245 1422691 +TMEM88B 1 1426128 1427787 +RP4-758J18.10 1 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+AJ271736.10 23 156004218 156022236 +AJ271736.5 23 156014623 156016837 +WASH6P 23 156020826 156025710 +DDX11L16 23 156025664 156027877 diff --git a/data/metadata/scGateDB/scGate_models-master/README.md b/data/metadata/scGateDB/scGate_models-master/README.md new file mode 100644 index 0000000..5758ff9 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/README.md @@ -0,0 +1,6 @@ +# scGate_models +Cell type gating models for scGate + +Note: we will tag versions of this DB using the format 'vN.M' (e.g. v1.4). This format is understood by scGate to download specific versions of the DB + +We are doing test over this database with [BiocFileCache](https://bioconductor.org/packages/release/bioc/html/BiocFileCache.html) as the solution to scGate data management for Bioconductor trasformation. \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/CTL_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/CTL_scGate_Model.tsv new file mode 100644 index 0000000..6bf2931 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/CTL_scGate_Model.tsv @@ -0,0 +1,5 @@ +levels use_as name signature +level1 positive Exh +level1 negative Treg +level1 negative NaiveLike +level2 positive CTL diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/NaiveLike_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/NaiveLike_scGate_Model.tsv new file mode 100644 index 0000000..7a094ae --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/NaiveLike_scGate_Model.tsv @@ -0,0 +1,6 @@ +levels use_as name signature +level1 positive NaiveLike +level1 negative Treg +level1 negative Exh +level1 negative Tfh +level1 negative Th1_Gzmk \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Tfh_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Tfh_scGate_Model.tsv new file mode 100644 index 0000000..88f66bd --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Tfh_scGate_Model.tsv @@ -0,0 +1,6 @@ +levels use_as name signature +level1 positive Exh +level1 negative Treg +level2 positive Tfh +level2 negative CTL +level2 negative Th17 \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th17_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th17_scGate_Model.tsv new file mode 100644 index 0000000..70fbc17 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th17_scGate_Model.tsv @@ -0,0 +1,7 @@ +levels use_as name signature +level1 positive Th17 +level1 negative Treg +level1 negative Exh +level1 negative Tfh +level1 negative CTL +level1 negative NaiveLike \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th1_Gzmk_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th1_Gzmk_scGate_Model.tsv new file mode 100644 index 0000000..7734c9e --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Th1_Gzmk_scGate_Model.tsv @@ -0,0 +1,4 @@ +levels use_as name signature +level1 positive Th1_Gzmk +level1 negative Treg +level1 negative Exh diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Treg_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Treg_scGate_Model.tsv new file mode 100644 index 0000000..ce26ef6 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/Treg_scGate_Model.tsv @@ -0,0 +1,2 @@ +levels use_as name signature +level1 positive Treg diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/master_table.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/master_table.tsv new file mode 100644 index 0000000..719fca4 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD4_TIL/master_table.tsv @@ -0,0 +1,8 @@ +name signature +Treg FOXP3 +Exh CXCL13;PDCD1;TOX +NaiveLike CCR7;LEF1;TCF7;SELL +Tfh IL21;CD200;CXCL13;TOX;TOX2 +CTL GZMB;PRF1;GZMA;NKG7;CCL5 +Th1_Gzmk GZMK;EOMES;CRTAM +Th17 IL17A;IL17F;RORC;CTSH;KLRB1;CCL20;IL26 \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/.DS_Store b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/.DS_Store new file mode 100644 index 0000000..273f91d Binary files /dev/null and b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/.DS_Store differ diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_EM_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_EM_scGate_Model.tsv new file mode 100644 index 0000000..e9d995d --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_EM_scGate_Model.tsv @@ -0,0 +1,9 @@ +levels use_as name signature +1 level1 positive CD8_EM +2 level1 negative CD8_TEMRA +3 level1 negative Exhausted +4 level1 negative CD8_TRM +5 level1 negative CD8_INN +6 level1 negative CD8_INN2 +7 level1 negative CD8_GD +8 level1 negative CD8_MAIT diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_MAIT_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_MAIT_scGate_Model.tsv new file mode 100644 index 0000000..ff976bc --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_MAIT_scGate_Model.tsv @@ -0,0 +1,2 @@ +"levels" "use_as" "name" "signature" +"1" "level1" "positive" "CD8_MAIT" "TRAV1-2;SLC4A10;KLRB1" diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_N_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_N_scGate_Model.tsv new file mode 100644 index 0000000..b7c07c2 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_N_scGate_Model.tsv @@ -0,0 +1,7 @@ +levels use_as name signature +level1 positive CD8_N +level1 negative Cytotoxic +level1 negative Exhausted +level1 negative CD8_INN +level1 negative CD8_INN2 +level1 negative CD8_GD diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEMRA_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEMRA_scGate_Model.tsv new file mode 100644 index 0000000..a0b945b --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEMRA_scGate_Model.tsv @@ -0,0 +1,5 @@ +levels use_as name signature +level1 positive CD8_TEMRA +level1 negative CD8_INN +level1 negative CD8_INN2 +level1 negative CD8_GD diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEX_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEX_scGate_Model.tsv new file mode 100644 index 0000000..0283f0b --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TEX_scGate_Model.tsv @@ -0,0 +1,7 @@ +levels use_as name signature +level1 positive Exhausted +level2 positive CD8_TEX +level2 negative CD8_TPEX +level2 negative CD8_INN +level2 negative CD8_INN2 +level2 negative CD8_GD diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TPEX_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TPEX_scGate_Model.tsv new file mode 100644 index 0000000..f95c28c --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TPEX_scGate_Model.tsv @@ -0,0 +1,3 @@ +levels use_as name signature +level1 positive Exhausted +level2 positive CD8_TPEX diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TRM_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TRM_scGate_Model.tsv new file mode 100644 index 0000000..cc1dc02 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_TRM_scGate_Model.tsv @@ -0,0 +1,7 @@ +levels use_as name signature +1 level1 positive CD8_TRM +2 level1 negative Exhausted +3 level1 negative CD8_INN +4 level1 negative CD8_INN2 +5 level1 negative CD8_GD +6 level1 negative CD8_MAIT diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_Tinn_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_Tinn_scGate_Model.tsv new file mode 100644 index 0000000..3f0a652 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/CD8_Tinn_scGate_Model.tsv @@ -0,0 +1,4 @@ +levels use_as name signature +level1 positive CD8_INN +level1 positive CD8_INN2 +level1 positive CD8_GD diff --git a/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/master_table.tsv b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/master_table.tsv new file mode 100644 index 0000000..0720b7a --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/CD8_TIL/master_table.tsv @@ -0,0 +1,13 @@ +name signature +Exhausted TOX;PDCD1;LAG3;TIGIT +Cytotoxic GZMB;GZMA;GZMK +CD8_N LEF1;CCR7;TCF7;SELL +CD8_TEX HAVCR2;LAYN;CXCL13;GZMB +CD8_TPEX XCL1;CD200;TCF7;CRTAM +CD8_TEMRA FCGR3A;CX3CR1;FGFBP2 +CD8_INN FCER1G;IKZF2;TYROBP +CD8_INN2 KIR2DL3;KLRC3;KIR3DL2;KLRC2 +CD8_EM GZMK;CXCR3 +CD8_GD TRDC;TRGC1;TRGC2;TRDV1 +CD8_TRM ZNF683;ITGAE;CXCR6 +CD8_MAIT TRAV1-2;SLC4A10;KLRB1 \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Bcell_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Bcell_scGate_Model.tsv new file mode 100644 index 0000000..a456c33 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Bcell_scGate_Model.tsv @@ -0,0 +1,18 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive PanBcell +level1 positive Bcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Bcell +level2 positive PanBcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive PanBcell +level3 positive Bcell +level3 negative Tcell +level3 negative NK +level4 positive Bcell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/CD4T_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/CD4T_scGate_Model.tsv new file mode 100644 index 0000000..b1ed3bc --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/CD4T_scGate_Model.tsv @@ -0,0 +1,20 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell +level4 positive CD4Tcell CD4;CD40LG;CD2;CD3D;CD3E;CD3G +level4 positive Treg +level4 negative CD8T +level4 negative Tgammadelta \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/CD8T_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/CD8T_scGate_Model.tsv new file mode 100644 index 0000000..e85f557 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/CD8T_scGate_Model.tsv @@ -0,0 +1,22 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level1 negative Erythrocyte +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level2 negative Erythrocyte +level3 positive Tcell +level3 positive Talphabeta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell +level4 positive CD8T +level4 negative Tgammadelta +level4 negative CD4T +level4 negative Treg \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/CD8_TIL_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/CD8_TIL_scGate_Model.tsv new file mode 100644 index 0000000..84bfee3 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/CD8_TIL_scGate_Model.tsv @@ -0,0 +1,24 @@ +levels use_as name signature +level1 positive Immune PTPRC +level1 positive Lymphoid LCK +level1 positive Tcell CD3D;CD3E;CD3G;CD2 +level1 negative Epithelial EPCAM;CLDN7 +level1 negative Stromal MMP2;COL1A1;COL1A2;COL5A1;LUM;PDGFRA +level1 negative Erythrocyte HBB;HBA2;HBA1 +level2 positive Lymphoid LCK +level2 positive Tcell CD3D;CD3E;CD3G;CD2 +level2 negative Myeloid SPI1 +level2 negative MoMacDC LYZ;CSF1R;MSR1;MAFB;CD300E +level2 negative Neutrophils CSF3R;FCGR3B;ANXA2- +level2 negative Erythrocyte HBB;HBA2;HBA1 +level3 positive Tcell CD3D;CD3E;CD3G;CD2 +level3 positive Talphabeta TRAC;TRBC1;TRBC2 +level3 negative Bcell MS4A1;BANK1;PAX5;CD19 +level3 negative NK KLRD1;NKG7;NCR1;FCGR3A;CD3D-;CD3E-;CD3G-;CD8A-;CD8B- +level3 negative Plasma_cell IGKC;IGHG3;IGHG1;IGHA1;CD19- +level4 positive CD8T CD8A;CD8B +level4 negative Tgammadelta TRDC;TRGC1;TRGC2;TRDV1 +level4 negative CD4T CD4;CD40LG +level4 negative Treg FOXP3 +level4 negative CD8_INN FCER1G;IKZF2;TYROBP +level4 negative CD8_INN2 KIR2DL3;KLRC3;KIR3DL2;KLRC2 diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Erythrocyte_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Erythrocyte_scGate_Model.tsv new file mode 100644 index 0000000..5660acc --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Erythrocyte_scGate_Model.tsv @@ -0,0 +1,7 @@ +levels use_as name signature +level1 positive Immune +level1 positive Erythrocyte +level1 negative Epithelial +level1 negative Stromal +level2 positive Erythrocyte +level2 negative Lymphoid \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Megakaryocyte_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Megakaryocyte_scGate_Model.tsv new file mode 100644 index 0000000..5acea28 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Megakaryocyte_scGate_Model.tsv @@ -0,0 +1,8 @@ +levels use_as name signature +level1 positive Immune +level1 positive Megakaryocyte +level1 negative Epithelial +level1 negative Stromal +level1 negative Erythrocyte +level2 positive Megakaryocyte +level2 negative Lymphoid \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/MoMacDC_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/MoMacDC_scGate_Model.tsv new file mode 100644 index 0000000..225f0b0 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/MoMacDC_scGate_Model.tsv @@ -0,0 +1,21 @@ +levels use_as name signature +level1 positive Immune +level1 positive Myeloid +level1 positive Macrophage +level1 positive pDC +level1 positive MoMacDC +level1 negative Epithelial +level1 negative Stromal +level1 negative Erythrocyte +level2 positive Macrophage +level2 positive Myeloid +level2 positive MoMacDC +level2 negative Lymphoid +level2 negative Tcell +level2 negative Bcell +level2 negative Plasma_cell +level2 negative PanBcell +level3 positive Macrophage +level3 positive Myeloid +level3 positive MoMacDC +level3 negative Neutrophils2 CSF3R;FCGR3B;ANXA2- \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Myeloid_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Myeloid_scGate_Model.tsv new file mode 100644 index 0000000..2abe6d2 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Myeloid_scGate_Model.tsv @@ -0,0 +1,19 @@ +levels use_as name signature +level1 positive Immune +level1 positive Myeloid +level1 positive Macrophage +level1 positive Neutrophils +level1 positive pDC +level1 positive MoMacDC +level1 negative Epithelial +level1 negative Stromal +level1 negative Erythrocyte +level2 positive Macrophage +level2 positive Myeloid +level2 positive MoMacDC +level2 positive Neutrophils +level2 negative Tcell +level2 negative Bcell +level2 negative Plasma_cell +level2 negative PanBcell +level2 negative Lymphoid \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/NK_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/NK_scGate_Model.tsv new file mode 100644 index 0000000..eeb8e71 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/NK_scGate_Model.tsv @@ -0,0 +1,14 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive NK +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level2 negative Monocyte +level3 positive NK +level3 negative Bcell +level3 negative Plasma_cell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/PanBcell_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/PanBcell_scGate_Model.tsv new file mode 100644 index 0000000..322fc43 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/PanBcell_scGate_Model.tsv @@ -0,0 +1,20 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive PanBcell +level1 positive Bcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Bcell +level2 positive PanBcell +level2 positive Plasma_cell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level2 negative pDC +level3 positive PanBcell +level3 positive Plasma_cell +level3 positive Bcell +level3 negative Tcell +level3 negative NK \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Plasma_cell_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Plasma_cell_scGate_Model.tsv new file mode 100644 index 0000000..307adf3 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Plasma_cell_scGate_Model.tsv @@ -0,0 +1,20 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive PanBcell +level1 positive Plasma_cell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive PanBcell +level2 positive Plasma_cell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive PanBcell +level3 positive Plasma_cell +level3 negative Tcell +level3 negative Bcell +level3 negative NK +level4 positive Plasma_cell +level4 negative Bcell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell.alphabeta_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell.alphabeta_scGate_Model.tsv new file mode 100644 index 0000000..36f05c3 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell.alphabeta_scGate_Model.tsv @@ -0,0 +1,17 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 negative Tgammadelta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell_scGate_Model.tsv new file mode 100644 index 0000000..c002fcb --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/Tcell_scGate_Model.tsv @@ -0,0 +1,17 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 positive Tgammadelta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/master_table.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/master_table.tsv new file mode 100644 index 0000000..b301f9c --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/master_table.tsv @@ -0,0 +1,29 @@ +name signature +Immune PTPRC +Lymphoid LCK +Myeloid SPI1 +Tcell CD3D;CD3E;CD3G;CD2 +Macrophage CD40;CD68;CD14;FCGR1A +MoMacDC LYZ;CSF1R;MSR1;MAFB;CD300E +Endothelial CDH5;ERG;CLDN5 +Epithelial EPCAM;CLDN7 +Stromal MMP2;COL1A1;COL1A2;COL5A1;LUM;PDGFRA +Bcell MS4A1;BANK1;PAX5;CD19 +Plasma_cell IGKC;IGHG3;IGHG1;IGHA1;CD19- +PanBcell CD79A +NK KLRD1;NKG7;NCR1;FCGR3A;CD3D-;CD3E-;CD3G-;CD8A-;CD8B- +Neutrophils CSF3R;FCGR3B;ANXA2- +CD4T CD4;CD40LG +Treg FOXP3 +CD8T CD8A;CD8B +Tgammadelta TRDC;TRGC1;TRGC2;TRDV1 +Talphabeta TRAC;TRBC1;TRBC2 +Erythrocyte HBB;HBA2;HBA1 +Megakaryocyte ITGA2B;PF4;PPBP +pDC IRF7;LILRA4;TCF4;GZMB +cDC1 CLEC9A;XCR1;BATF3;CADM1;IDO1;RAB7B;FLT3;IRF8 +cDC2 CLEC10A;CD1A;CD1C;S100B +DC3 LAMP3;FSCN1;CCL19;CCL22;CD274;CCR7;BIRC3 +Monocyte S100A9;S100A8;FCN1;VCAN +Monocyte2 S100A9;S100A8;FCN1;VCAN;CLEC10A-;CD1A;CD1C-;S100B- +Mast KIT;CPA3;GATA2 \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/human/generic/panDC_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/human/generic/panDC_scGate_Model.tsv new file mode 100644 index 0000000..2c31a73 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/human/generic/panDC_scGate_Model.tsv @@ -0,0 +1,28 @@ +levels use_as name signature +level1 positive Immune +level1 positive Myeloid +level1 positive MoMacDC +level1 positive pDC +level1 positive cDC1 +level1 positive cDC2 +level1 positive DC3 +level1 negative Epithelial +level1 negative Endothelial +level1 negative Stromal +level2 positive pDC +level2 positive cDC1 +level2 positive cDC2 +level2 positive DC3 +level2 negative Lymphoid +level2 negative Tcell +level2 negative Bcell +level2 negative PanBcell +level2 negative Mast +level2 negative NK +level3 positive pDC +level3 positive cDC1 +level3 positive cDC2 +level3 positive DC3 +level3 negative Monocyte2 +level3 negative Neutrophils +level3 negative Tcell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD4T_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD4T_scGate_Model.tsv new file mode 100644 index 0000000..357c609 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD4T_scGate_Model.tsv @@ -0,0 +1,20 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell +level4 positive CD4T +level4 positive Treg +level4 negative CD8T +level4 negative Tgammadelta \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD8T_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD8T_scGate_Model.tsv new file mode 100644 index 0000000..e85f557 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/CD8T_scGate_Model.tsv @@ -0,0 +1,22 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level1 negative Erythrocyte +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level2 negative Erythrocyte +level3 positive Tcell +level3 positive Talphabeta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell +level4 positive CD8T +level4 negative Tgammadelta +level4 negative CD4T +level4 negative Treg \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/MoMacDC_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/MoMacDC_scGate_Model.tsv new file mode 100644 index 0000000..fb5b052 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/MoMacDC_scGate_Model.tsv @@ -0,0 +1,31 @@ +levels use_as name signature +level1 positive Immune +level1 positive Myeloid +level1 positive Macrophage +level1 positive MoMacDC +level1 positive pDC +level1 positive DC1 Xcr1;Cadm1;Itgae +level1 positive DC2 Lilrb4;Csf1r;Itgax +level1 positive DC3 Fscn1;Ccr7 +level1 positive MonoDC Clec10a;Fit3 +level1 negative Epithelial +level2 positive Myeloid +level2 positive Macrophage +level2 positive MoMacDC +level2 positive pDC +level2 positive DC1 Xcr1;Cadm1;Itgae +level2 positive DC2 Lilrb4;Csf1r;Itgax +level2 positive DC3 Fscn1;Ccr7 +level2 positive MonoDC Clec10a;Fit3 +level2 negative Lymphoid +level2 negative Tcell +level2 negative Bcell +level2 negative Plasma_cell +level2 negative PanBcell +level3 positive Macrophage +level3 positive MoMacDC +level3 positive pDC +level3 positive DC1 Xcr1;Cadm1;Itgae +level3 positive DC2 Lilrb4;Csf1r;Itgax +level3 positive DC3 Fscn1;Ccr7 +level3 positive MonoDC Clec10a;Fit3 \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/Myeloid_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Myeloid_scGate_Model.tsv new file mode 100644 index 0000000..8d8a049 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Myeloid_scGate_Model.tsv @@ -0,0 +1,19 @@ +levels use_as name signature +level1 positive Immune +level1 positive Myeloid +level1 positive Macrophage +level1 positive Neutrophils +level1 positive MoMacDC +level1 positive pDC +level1 negative Epithelial +level1 negative Stromal +level2 positive Myeloid +level2 positive Macrophage +level2 positive Neutrophils +level2 positive MoMacDC +level2 positive pDC +level2 negative Lymphoid +level2 negative Tcell +level2 negative Bcell +level2 negative Plasma_cell +level2 negative PanBcell \ No newline at end of file diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell.alphabeta_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell.alphabeta_scGate_Model.tsv new file mode 100644 index 0000000..af2355c --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell.alphabeta_scGate_Model.tsv @@ -0,0 +1,17 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 negative Tgammadelta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell_scGate_Model.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell_scGate_Model.tsv new file mode 100644 index 0000000..c002fcb --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/Tcell_scGate_Model.tsv @@ -0,0 +1,17 @@ +levels use_as name signature +level1 positive Immune +level1 positive Lymphoid +level1 positive Tcell +level1 negative Epithelial +level1 negative Stromal +level2 positive Lymphoid +level2 positive Tcell +level2 negative Myeloid +level2 negative MoMacDC +level2 negative Neutrophils +level3 positive Tcell +level3 positive Talphabeta +level3 positive Tgammadelta +level3 negative Bcell +level3 negative NK +level3 negative Plasma_cell diff --git a/data/metadata/scGateDB/scGate_models-master/mouse/generic/master_table.tsv b/data/metadata/scGateDB/scGate_models-master/mouse/generic/master_table.tsv new file mode 100644 index 0000000..36fd1c2 --- /dev/null +++ b/data/metadata/scGateDB/scGate_models-master/mouse/generic/master_table.tsv @@ -0,0 +1,21 @@ +name signature +Immune Ptprc +Lymphoid Lck +Myeloid Spi1 +Tcell Cd3d;Cd3e;Cd3g;Cd2 +Macrophage Cd40;Cd68;Cd14;Fcgr1a +MoMacDC Lyz1;Lyz2;Csf1r;Msr1;Mafb;Cd300e +Epithelial Cdh1;Flt1 +Stromal Mmp2;Col1a1;Col1a2;Col5a1;Lum;Pdgfra +Bcell Ms4a1;Bank1;Pax5;Cd19 +Plasma_cell Igkc;Ighg3;Ighg1;Igha;Cd19- +PanBcell Cd79a +NK Klrd1;Nkg7;Ncr1;Cd3d-;Cd3e-;Cd3g-;Cd8a-;Cd8b1- +Neutrophils Csf3r;S100a9;S100a8;Mmp9 +CD4T Cd4;Cd40lg +Treg Foxp3 +CD8T Cd8a;Cd8b1 +Tgammadelta Trdc;Trgc1;Trgc2;Trdv1 +Talphabeta Trac;Trbc1;Trbc2 +pDC Ccr9;Siglech +Erythrocyte Hba-a1;Hba-a2 \ No newline at end of file diff --git a/data/metadata/signatures/cellCycleMarkers.R b/data/metadata/signatures/cellCycleMarkers.R new file mode 100644 index 0000000..e0430e0 --- /dev/null +++ b/data/metadata/signatures/cellCycleMarkers.R @@ -0,0 +1,13 @@ +# DOI: 10.1016/j.cell.2018.09.006 -> DOI: 10.1126/science.aad0501 +s.genes = c("ATAD2", "BLM", "BRIP1", "CASP8AP2", "CCNE2", "CDC45", "CDC6", "CDCA7", + "CHAF1B", "CLSPN", "DSCC1", "DTL", "E2F8", "EXO1", "FEN1", "GINS2", "GMNN", "HELLS", + "MCM2", "MCM4", "MCM5", "MCM6", "MLF1IP", "MSH2", "NASP", "PCNA", "POLA1", "POLD3", + "PRIM1", "RAD51", "RAD51AP1", "RFC2", "RPA2", "RRM1", "RRM2", "SLBP", "TIPIN", + "TYMS", "UBR7", "UHRF1", "UNG", "USP1", "WDR76") + +g2m.genes = c("ANLN", "ANP32E", "AURKA", "AURKB", "BIRC5", "BUB1", "CBX5", "CCNB2", + "CDC20", "CDC25C", "CDCA2", "CDCA3", "CDCA8", "CDK1", "CENPA", "CENPE", "CENPF", + "CKAP2", "CKAP2L", "CKAP5", "CKS1B", "CKS2", "CTCF", "DLGAP5", "ECT2", "FAM64A", + "G2E3", "GAS2L3", "GTSE1", "HJURP", "HMGB2", "HMMR", "HN1", "KIF11", "KIF20B", + "KIF23", "KIF2C", "LBR", "MKI67", "NCAPD2", "NDC80", "NEK2", "NUF2", "NUSAP1", + "PSRC1", "RANGAP1", "SMC4", "TACC3", "TMPO", "TOP2A", "TPX2", "TTK", "TUBB4B", "UBE2C") diff --git a/manifest.yaml b/manifest.yaml new file mode 100644 index 0000000..ba30d1b --- /dev/null +++ b/manifest.yaml @@ -0,0 +1,14 @@ +# project and workdata paths +base: + homes: "" + workdata: "/" + +grieb: + fastq: "" + work: "/cohorts/grieb/" + data_dl: "/cohorts/grieb/" + seurat: "/cohorts/grieb/seurat/" + phenodata: "data/clinicopathological_discovery.csv" + +meta: + work: "/meta/"