diff --git a/.github/workflows/docker_cell-type-ewings.yml b/.github/workflows/docker_cell-type-ewings.yml index 86474a96a..125c57b1f 100644 --- a/.github/workflows/docker_cell-type-ewings.yml +++ b/.github/workflows/docker_cell-type-ewings.yml @@ -40,7 +40,7 @@ jobs: test-build: name: Test Build Docker Image if: github.event_name == 'pull_request' || (contains(github.event_name, 'workflow_') && !inputs.push-ecr) - runs-on: ubuntu-latest + runs-on: openscpca-22.04-big-disk steps: - name: Set up Docker Buildx @@ -49,7 +49,7 @@ jobs: - name: Build image uses: docker/build-push-action@v6 with: - context: "{{defaultContext}}:analyses/simulate-sce" + context: "{{defaultContext}}:analyses/cell-type-ewings" push: false cache-from: type=gha cache-to: type=gha,mode=max diff --git a/.github/workflows/docker_metacells.yml b/.github/workflows/docker_metacells.yml index 03728071f..c2646121e 100644 --- a/.github/workflows/docker_metacells.yml +++ b/.github/workflows/docker_metacells.yml @@ -13,14 +13,14 @@ concurrency: cancel-in-progress: true on: - # pull_request: - # branches: - # - main - # paths: - # - "analyses/metacells/Dockerfile" - # - "analyses/metacells/.dockerignore" - # - "analyses/metacells/renv.lock" - # - "analyses/metacells/conda-lock.yml" + pull_request: + branches: + - main + paths: + - "analyses/metacells/Dockerfile" + - "analyses/metacells/.dockerignore" + - "analyses/metacells/renv.lock" + - "analyses/metacells/conda-lock.yml" workflow_dispatch: inputs: push-ecr: diff --git a/.github/workflows/run_cell-type-ETP-ALL-03.yml b/.github/workflows/run_cell-type-ETP-ALL-03.yml index 1ad55dcbc..003087fbb 100644 --- a/.github/workflows/run_cell-type-ETP-ALL-03.yml +++ b/.github/workflows/run_cell-type-ETP-ALL-03.yml @@ -87,6 +87,19 @@ jobs: run: | cd ${MODULE_PATH} # run module script(s) here + printf "\n\nRunning 00-01_processing_rds.R\n" Rscript scripts/00-01_processing_rds.R + printf "\n\nRunning 02-03_annotation.R\n" Rscript scripts/02-03_annotation.R - Rscript scripts/multipanel_plot.R + printf "\n\nRunning 04_multipanel_plot.R\n" + Rscript scripts/04_multipanel_plot.R + printf "\n\nRunning 05_cluster_evaluation.R\n" + Rscript scripts/05_cluster_evaluation.R + printf "\n\nRunning 06_sctype_exploration.R\n" + Rscript scripts/06_sctype_exploration.R + printf "\n\nRunning 07_run_copykat.R\n" + Rscript scripts/07_run_copykat.R + printf "\n\nRunning markerGenes_submission.R\n" + Rscript scripts/markerGenes_submission.R + printf "\n\nRunning writeout_submission.R\n" + Rscript scripts/writeout_submission.R diff --git a/.github/workflows/run_cell-type-nonETP-ALL-03.yml b/.github/workflows/run_cell-type-nonETP-ALL-03.yml index 7880d3596..6315533c5 100644 --- a/.github/workflows/run_cell-type-nonETP-ALL-03.yml +++ b/.github/workflows/run_cell-type-nonETP-ALL-03.yml @@ -59,7 +59,8 @@ jobs: libfontconfig1-dev \ libharfbuzz-dev \ libfribidi-dev \ - libtiff5-dev + libtiff5-dev \ + jags - name: Set up renv uses: r-lib/actions/setup-renv@v2 @@ -87,7 +88,19 @@ jobs: run: | cd ${MODULE_PATH} # run module script(s) here + printf "\n\nRunning 00-01_processing_rds.R\n" Rscript scripts/00-01_processing_rds.R + printf "\n\nRunning 02-03_annotation.R\n" Rscript scripts/02-03_annotation.R + printf "\n\nRunning 04_multipanel_plot.R\n" Rscript scripts/04_multipanel_plot.R + printf "\n\nRunning 05_cluster_evaluation.R\n" Rscript scripts/05_cluster_evaluation.R + printf "\n\nRunning 06_sctype_exploration.R\n" + Rscript scripts/06_sctype_exploration.R + printf "\n\nRunning 07_run_copykat.R\n" + Rscript scripts/07_run_copykat.R + printf "\n\nRunning markerGenes_submission.R\n" + Rscript scripts/markerGenes_submission.R + printf "\n\nRunning writeout_submission.R\n" + Rscript scripts/writeout_submission.R diff --git a/.github/workflows/run_cell-type-wilms-tumor-14.yml b/.github/workflows/run_cell-type-wilms-tumor-14.yml index ff97675cb..dd5c74fe7 100644 --- a/.github/workflows/run_cell-type-wilms-tumor-14.yml +++ b/.github/workflows/run_cell-type-wilms-tumor-14.yml @@ -33,41 +33,22 @@ jobs: run-module: if: github.repository_owner == 'AlexsLemonade' runs-on: ubuntu-latest + container: public.ecr.aws/openscpca/cell-type-wilms-tumor-14:latest steps: - - name: Checkout repo - uses: actions/checkout@v4 - - - name: Set up R - uses: r-lib/actions/setup-r@v2 - with: - r-version: 4.4.0 - use-public-rspm: true - - - name: Set up pandoc - uses: r-lib/actions/setup-pandoc@v2 - - - name: Install system dependencies + - name: Install git run: | - sudo apt-get install -y \ - jags \ - libcurl4-openssl-dev \ - libfribidi-dev \ - libglpk40 \ - libharfbuzz-dev \ - libhdf5-dev \ - libmagick++-dev \ - libtiff5-dev + apt-get update + apt-get install -y git - - name: Set up renv - uses: r-lib/actions/setup-renv@v2 - with: - working-directory: ${{ env.MODULE_PATH }} - - - name: Initialize zellkonverter environment + - name: Install aws-cli run: | - cd ${MODULE_PATH} - Rscript -e "proc <- basilisk::basiliskStart(env = zellkonverter::zellkonverterAnnDataEnv(), testload = 'anndata'); basilisk::basiliskStop(proc)" + curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip" + unzip -q awscliv2.zip + ./aws/install + + - name: Checkout repo + uses: actions/checkout@v4 # Update this step as needed to download the desired data - name: Download test data and results diff --git a/.github/workflows/test_ropenscpca.yml b/.github/workflows/test_ropenscpca.yml deleted file mode 100644 index 0ae3f7176..000000000 --- a/.github/workflows/test_ropenscpca.yml +++ /dev/null @@ -1,38 +0,0 @@ -on: - pull_request: - branches: - - main - - feature/* - paths: - - packages/rOpenScPCA/** - -name: Check the rOpenScPCA package - -jobs: - R-CMD-check-renv: - runs-on: ubuntu-22.04 - env: - GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }} - steps: - - name: Checkout repo - uses: actions/checkout@v4 - - - name: Set up R - uses: r-lib/actions/setup-r@v2 - with: - r-version: 4.4.0 - use-public-rspm: true - - - name: Set up dependencies - uses: r-lib/actions/setup-r-dependencies@v2 - with: - working-directory: "packages/rOpenScPCA/" - extra-packages: | - any::rcmdcheck - needs: check - - - name: Check package - uses: r-lib/actions/check-r-package@v2 - with: - working-directory: "packages/rOpenScPCA/" - args: 'c("--no-manual")' diff --git a/analyses/cell-type-ETP-ALL-03/README.md b/analyses/cell-type-ETP-ALL-03/README.md index d713df3ab..8dbdff6cb 100644 --- a/analyses/cell-type-ETP-ALL-03/README.md +++ b/analyses/cell-type-ETP-ALL-03/README.md @@ -1,6 +1,6 @@ # ETP T-ALL Annotation (SCPCP000003) -This analysis module will include codes to annotate cell types and tumor/normal status in ETP T-ALL from SCPCP000003 (n=30) present on the ScPCA portal. +This analysis module will include codes to annotate cell types and tumor/normal status in ETP T-ALL from SCPCP000003 (n=31) present on the ScPCA portal. ## Description @@ -8,9 +8,11 @@ We first aim to annotate the cell types in ETP T-ALL, and use the annotated B ce - We use the cell type marker (`Azimuth_BM_level1.csv`) from [Azimuth Human Bone Marrow reference](https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow). In total, there are 14 cell types: B, CD4T, CD8T, Other T, DC, Monocytes, Macrophages, NK, Early Erythrocytes, Late Erythrocytes, Plasma, Platelet, Stromal, and Hematopoietic Stem and Progenitor Cells (HSPC). Based on the exploratory analysis, we believe that most of the cells in these samples do not express adequate markers to be distinguished at finer cell type level (eg. naive vs memory, CD14 vs CD16 etc.), and majority of the cells should belong to T-cells. In addition, we include the marker genes for blast cell [[Bhasin et al. (2023)](https://www.nature.com/articles/s41598-023-39152-z)] as well as erythroid precursor and cancer cell in immune system [[ScType](https://sctype.app/database.php) database]. + \*\*Azimuth_BM_level1.csv is converted to submission_markerGenes.tsv, in the final submission format. + - Since ScType annotates cell types at cluster level using marker genes provided by user or from the built-in database, we employ [self-assembling manifold](https://github.com/atarashansky/self-assembling-manifold/tree/master) (SAM) algorithm, a soft feature selection strategy for better separation of homogeneous cell types. -- After cell type annotation, we provide B cells as the normal cells in the sample, if there is any, to [CopyKat](https://github.com/navinlabcode/copykat), for identification of tumor cells. +- After cell type annotation, we fine-tune the annotated B cells by applying 99 percentile cutoff of non-B ScType score on the "B cell clusters". We then use the new B cells (i.e those cells which passed the cutoff) as the normal cells in running [CopyKat](https://github.com/navinlabcode/copykat), for the identification of tumor cells. Here are the steps in the module: @@ -18,6 +20,10 @@ Here are the steps in the module: 2. Annotating cell type using ScType and identifying tumor cells using CopyKat (`scripts/02-03_annotation.R`) +3. Fine-tuning the B cells (`scripts/06_sctype_exploration.R`) + +4. Re-running CopyKat (`scripts/07_run_copykat.R`) + ## Usage Before running Rscripts in R or Rstudio, we first need to prepare the input files as shown in the next section, and run the following codes in the terminal for installing required libraries: @@ -27,6 +33,7 @@ Before running Rscripts in R or Rstudio, we first need to prepare the input file sudo apt install libglpk40 sudo apt install libcurl4-openssl-dev #for Seurat sudo apt-get install libxml2-dev libfontconfig1-dev libharfbuzz-dev libfribidi-dev libtiff5-dev #for devtools +sudo apt-get install r-cran-rjags #for InferCNV, if wish to run conda-lock install --name openscpca-cell-type-ETP-ALL-03 conda-lock.yml Rscript -e "renv::restore()" @@ -44,21 +51,15 @@ The `scripts/00-01_processing_rds.R` requires the processed SingleCellExperiment As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `ensembl_id_positive_marker` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `ensembl_id_negative_marker`. -## Output files - -Running `scripts/00-01_processing_rds.R` will generate two types of output: - -- `rds` objects in `scratch/` - -- umap plots showing leiden clustering in `plots/` +## Important output files -Running `scripts/02-03_annotation.R` will generate several outputs: +- `rds` objects in `results/rds` -- updated `rds` objects in `scratch/` +- ScType results of top 10 possible cell types in a cluster (`results/_sctype_top10_celltypes_perCluster.txt`) and ScType score (`results/_sctype_scores.txt`) -- umap plots showing cell type and CopyKat prediction (if there is any) and dotplots showing the features added with `AddModuleScore()` in `plots/` +- location of fine-tuned B cells in umap (`plots/sctype_exploration/_newBcells.png`) and the cell type assignment with added fine-tuned B cells (`results/_newB-normal-annotation.txt`) -- ScType results of top 10 possible cell types in a cluster (`_sctype_top10_celltypes_perCluster.txt`) and metadata file tabulating leiden cluster, cell type, low confidence cell type, and CopyKat prediction for each cell (`_metadata.txt`) in `results/` +- final submission table (`results/submission_table/_metadata.tsv`) and the umap plots showing cell_type_assignment from ScType and tumor_cell_classification from CopyKat using fine-tuned B cells (`results/submission_table/multipanels_.png`) ## Software requirements diff --git a/analyses/cell-type-ETP-ALL-03/plots/copykat_exploration/SCPCL000055_blastModuleScore.png b/analyses/cell-type-ETP-ALL-03/plots/copykat_exploration/SCPCL000055_blastModuleScore.png new file mode 100644 index 000000000..cce74d0e8 Binary files /dev/null and b/analyses/cell-type-ETP-ALL-03/plots/copykat_exploration/SCPCL000055_blastModuleScore.png differ diff --git a/analyses/cell-type-ETP-ALL-03/plots/copykat_exploration/SCPCL000055_celltypeVScopykat.png 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"CRAN", + "Requirements": [ + "R", + "stats" + ], + "Hash": "9e8405eacb262c0a939e121650247f4b" + }, "nlme": { "Package": "nlme", "Version": "3.1-164", @@ -1748,6 +2191,28 @@ ], "Hash": "68a2d681e10cf72f0afa1d84d45380e5" }, + "pdfCluster": { + "Package": "pdfCluster", + "Version": "1.0-4", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "geometry", + "methods" + ], + "Hash": "51e3a7a4af0b863e5d380575cbd33cda" + }, + "phyclust": { + "Package": "phyclust", + "Version": "0.1-34", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "ape" + ], + "Hash": "18a29354ea762dd01042d8697b7089d4" + }, "pillar": { "Package": "pillar", "Version": "1.9.0", @@ -1899,6 +2364,29 @@ ], "Hash": "017561f17632c065388b7062da030952" }, + "rOpenScPCA": { + "Package": "rOpenScPCA", + "Version": "0.1.0", + "Source": "GitHub", + "RemoteType": "github", + "RemoteHost": "api.github.com", + "RemoteUsername": "AlexsLemonade", + "RemoteRepo": "rOpenScPCA", + "RemoteRef": "main", + "RemoteSha": "f71a8191130fa543e6506c73c7b62ffa55e8ba3f", + "Requirements": [ + "BiocParallel", + "SingleCellExperiment", + "bluster", + "dplyr", + "methods", + "pdfCluster", + "purrr", + "tibble", + "tidyr" + ], + "Hash": "4b9dcd60f9ae2a2e1999acbd49d7b07a" + }, "rappdirs": { "Package": "rappdirs", "Version": "0.3.3", @@ -1977,6 +2465,17 @@ ], "Hash": "99e15369f8fb17dc188377234de13fc6" }, + "rjags": { + "Package": "rjags", + "Version": "4-16", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "coda" + ], + "Hash": "a36ff5b8df160527e29037be8e1cdf7d" + }, "rlang": { "Package": "rlang", "Version": "1.1.4", @@ -2021,6 +2520,19 @@ ], "Hash": "4c8415e0ec1e29f3f4f6fc108bef0144" }, + "sandwich": { + "Package": "sandwich", + "Version": "3.1-1", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "stats", + "utils", + "zoo" + ], + "Hash": "072bb2d27425f2a58fe71fe1080676ce" + }, "sass": { "Package": "sass", "Version": "0.4.9", @@ -2165,6 +2677,17 @@ ], "Hash": "c956d93f6768a9789edbc13072b70c78" }, + "snow": { + "Package": "snow", + "Version": "0.4-4", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "utils" + ], + "Hash": "40b74690debd20c57d93d8c246b305d4" + }, "sourcetools": { "Package": "sourcetools", "Version": "0.1.7-1", @@ -2336,6 +2859,18 @@ ], "Hash": "69b26ceb9f7976f347049b4d470c2d65" }, + "statmod": { + "Package": "statmod", + "Version": "1.5.0", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "graphics", + "stats" + ], + "Hash": "26e158d12052c279bdd4ba858b80fb1f" + }, "stringi": { "Package": "stringi", "Version": "1.8.4", @@ -2506,6 +3041,19 @@ ], "Hash": "c03fa420630029418f7e6da3667aac4a" }, + "viridis": { + "Package": "viridis", + "Version": "0.6.5", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "ggplot2", + "gridExtra", + "viridisLite" + ], + "Hash": "acd96d9fa70adeea4a5a1150609b9745" + }, "viridisLite": { "Package": "viridisLite", "Version": "0.4.2", diff --git a/analyses/cell-type-ETP-ALL-03/results/README.md b/analyses/cell-type-ETP-ALL-03/results/README.md index fdb40dd83..401358f1a 100644 --- a/analyses/cell-type-ETP-ALL-03/results/README.md +++ b/analyses/cell-type-ETP-ALL-03/results/README.md @@ -13,4 +13,20 @@ These are the generated outputs for each sample in the S3 bucket: - metadata and ScType results: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/` +- CopyKat results: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/copykat_output` + +- InferCNV results: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/infercnv_output` + +- evaluating cluster separation, stability, and purity: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/evalClus` + - umap and dot plots: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/plots` + +- violin and stacked bar plots for exploring the results of CopyKat prediction: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/plots/copykat_exploration` + +- ridge plots showing the ScType score for each cell type in annotated B cells from ScType, SingleR, and CellAssign, as well as the scatter plots showing the relationship between B cell ScType score and cluster purity of these cells: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/plots/sctype_exploration` + +- ridge plots showing the ScType score for each cell type in fine-tuned B cells and feature plots showing the distribution of B cell ScType score: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/plots/sctype_exploration` + +- final submission `tsv` files and `png` for cell type and/or tumor cell classification: `s3://researcher-650251722463-us-east-2/cell-type-ETP-ALL-03/results/submission_table` + +\*\*All the plots are also found in the repository plots/. diff --git a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R index 5b007aa0d..a0e004d4a 100644 --- a/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R +++ b/analyses/cell-type-ETP-ALL-03/scripts/02-03_annotation.R @@ -17,48 +17,6 @@ library(ggplot2) # load cell type annotation function source("https://github.com/IanevskiAleksandr/sc-type/raw/6db9eef49f185cf4d79bfec92a20fcf1edcccafb/R/sctype_score_.R") -gene_sets_prepare <- function(path_to_db_file, cell_type){ - cell_markers = read.csv(path_to_db_file, header = T) - cell_markers = cell_markers[cell_markers$tissueType == cell_type,] - cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker); cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) - - # correct gene symbols from the given DB (up-genes) - cell_markers$ensembl_id_positive_marker = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_positive_marker[i],","))) - markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) - markers_all = sort(markers_all) - - if(length(markers_all) > 0){ - suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here - paste0(markers_all, collapse=",") - } else { - "" - } - }) - - # correct gene symbols from the given DB (down-genes) - cell_markers$ensembl_id_negative_marker = sapply(1:nrow(cell_markers), function(i){ - markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_negative_marker[i],","))) - markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) - markers_all = sort(markers_all) - - if(length(markers_all) > 0){ - suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here - paste0(markers_all, collapse=",") - } else { - "" - } - }) - - cell_markers$ensembl_id_positive_marker = gsub("///",",",cell_markers$ensembl_id_positive_marker);cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker) - cell_markers$ensembl_id_negative_marker = gsub("///",",",cell_markers$ensembl_id_negative_marker);cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) - - gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_positive_marker[j]),",")))); names(gs) = cell_markers$cellName - gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_negative_marker[j]),",")))); names(gs2) = cell_markers$cellName - - list(gs_positive = gs, gs_negative = gs2) -} - running_scType <- function(gs_list, annot.obj, assay = "RNA", thres = 4){ # check Seurat object version (scRNA-seq matrix extracted differently in Seurat v4/v5) seurat_package_v5 <- isFALSE('counts' %in% names(attributes(annot.obj[[assay]]))); @@ -85,7 +43,7 @@ running_scType <- function(gs_list, annot.obj, assay = "RNA", thres = 4){ cl_type = sctype_scores[sctype_scores$cluster==cluster_num,]; annot.obj@meta.data$sctype_classification[annot.obj@meta.data$leiden_clusters == cluster_num] = as.character(cl_type$type[1]) } - return (list(annot.obj, cL_results)) + return (list(annot.obj, cL_results, es.max)) } plot_modulescore <- function(gs_list, seu, sample.name){ @@ -113,8 +71,10 @@ run_annot <- function(ind.lib){ res <- running_scType(gs_list, seu) seu <- res[[1]] #res[[2]] - A table with top 10 cell types with the highest scores for each cluster + #res[[3]] - A cell type x cells table tabulating the ScType score of each cell for all cell types write.table(res[[2]], file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_top10_celltypes_perCluster.txt")), row.names = F, sep = "\t", quote = F) + write.table(t(res[[3]]), file = file.path(out_loc,"results",paste0(ind.lib,"_sctype_scores.txt")), sep = "\t", quote = F) seu <- plot_modulescore(gs_list, seu, ind.lib) @@ -162,6 +122,8 @@ metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t metadata <- metadata[which(metadata$scpca_project_id == projectID & metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] libraryID <- metadata$scpca_library_id + +source(file.path(out_loc, "scripts/util/gene-set-functions.R")) # DB file db <- file.path(out_loc,"Azimuth_BM_level1.csv") tissue <- "Immune system" diff --git a/analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R b/analyses/cell-type-ETP-ALL-03/scripts/04_multipanel_plot.R similarity index 100% rename from analyses/cell-type-ETP-ALL-03/scripts/multipanel_plot.R rename to analyses/cell-type-ETP-ALL-03/scripts/04_multipanel_plot.R diff --git a/analyses/cell-type-ETP-ALL-03/scripts/05_cluster_evaluation.R b/analyses/cell-type-ETP-ALL-03/scripts/05_cluster_evaluation.R new file mode 100644 index 000000000..402a719ac --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/05_cluster_evaluation.R @@ -0,0 +1,51 @@ +#!/usr/bin/env Rscript + +## Calculates silhouette score and purity for each cluster and evaluates their stability, +## using the functions available in evaluate-cluster.R (on OpenscPCA portal) + +library(Seurat) +library(dplyr) + +run_eval <- function(ind.lib) { + seu <- readRDS(file.path(out_loc, "results/rds", paste0(ind.lib, ".rds"))) + clusID.df <- data.frame(FetchData(seu, vars = "leiden_clusters")) |> tibble::rownames_to_column(var = "cell_id") + colnames(clusID.df)[2] <- "cluster" + cluster_df1 <- rOpenScPCA::calculate_silhouette(x = seu, cluster_df = clusID.df, pc_name = "Xpca_") + cluster_df2 <- rOpenScPCA::calculate_purity(x = seu, cluster_df = clusID.df, pc_name = "Xpca_") + final_df <- merge(cluster_df1, cluster_df2, by = c("cell_id", "cluster")) + perClus_df <- final_df %>% + group_by(cluster) %>% + summarise(avgSil = mean(silhouette_width), avgPur = mean(purity)) %>% + data.frame() + stability_df <- rOpenScPCA::calculate_stability( + x = seu, cluster_df = clusID.df, + pc_name = "Xpca_", algorithm = "leiden", + resolution = 1.0, objective_function = "modularity", + seed = 10 + ) + write.table(final_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_sil-purity_perCell.txt")) + ) + write.table(stability_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_stability.txt")) + ) + write.table(perClus_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_avgSil-purity_perClus.txt")) + ) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current", projectID) +dir.create(file.path(out_loc, "results/evalClus"), showWarnings = FALSE) + +metadata <- read.table(file.path(data_loc, "single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +purrr::walk(libraryID, run_eval) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/06_sctype_exploration.R b/analyses/cell-type-ETP-ALL-03/scripts/06_sctype_exploration.R new file mode 100644 index 000000000..7272d07be --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/06_sctype_exploration.R @@ -0,0 +1,111 @@ +#!/usr/bin/env Rscript + +## This script investigates how solid are the B cells annotation, by checking the ScType score and the purity of that cluster + +library(ggridges) +library(ggplot2) +library(dplyr) +library(Seurat) + +Bcell_check <- function(ind.lib, methods = c("ScType","SingleR","CellAssign"), + variables.to.plot = c("sctype_classification","singler_celltype_annotation","cellassign_celltype_annotation")){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + sctype.score <- read.table(file.path(out_loc,"results",paste0(ind.lib,"_sctype_scores.txt")), + sep = "\t", header = T) + eval.clus <- read.table(file.path(out_loc,"results/evalClus/",paste0(ind.lib,"_sil-purity_perCell.txt")), + sep = "\t", header = T) + + plot.list <- list() + for (var_iter in 1:length(variables.to.plot)){ + plot.type <- variables.to.plot[var_iter] + if (plot.type == "sctype_classification"){ + Bcell.names <- colnames(seu)[which(seu$sctype_classification=="B")] + } else{ + celltype <- unique(seu@meta.data[[plot.type]]) + seu_df <- data.frame(FetchData(seu, vars = plot.type))|> tibble::rownames_to_column(var = "cell_id") + seu_df <- seu_df[which(seu_df[[plot.type]] %in% celltype[grep("B cell",celltype)]),] + Bcell.names <- seu_df$cell_id + } + + if (length(Bcell.names) == 0){next} + df <- sctype.score[match(Bcell.names, rownames(sctype.score)),] %>% + tidyr::pivot_longer(cols = colnames(sctype.score), names_to = "celltype", values_to = "ScType.score") + df$celltype <- gsub("\\."," ", df$celltype) + p1 <- ggplot(df, aes(x = ScType.score, y = forcats::fct_reorder(celltype,ScType.score), fill = celltype)) + + geom_density_ridges() + theme_ridges() + + theme(legend.position = "none", axis.title.x = element_text(hjust=0.5), axis.title.y = element_text(vjust=0.5)) + + scale_fill_manual(values = ct_color) + xlab ("ScType score") + + ylab(expr(bold(!!methods[var_iter])*~"("*!!length(Bcell.names)*")")) + + plot.df <- data.frame(cell_id=Bcell.names, + purity=eval.clus$purity[match(Bcell.names, eval.clus$cell_id)], + sctypeScore=sctype.score$B[match(Bcell.names, rownames(sctype.score))], + leidenCluster=as.factor(seu$leiden_clusters[match(Bcell.names,colnames(seu))])) + p2 <- ggplot(plot.df, aes(x = purity, y = sctypeScore, color = leidenCluster)) + + geom_point(size = 0.5) + theme_classic() + ylab("B cell ScType score") + xlim(0,1) + + plot.list <- c(plot.list, list(p1, p2)) + } + if (length(plot.list) == 0){return()} + cowplot::plot_grid(plotlist = plot.list, nrow = 3) + + patchwork::plot_annotation(title = paste0(ind.lib,": B cells identified in different methods")) & + theme(plot.title = element_text(hjust = 0.5, face="bold")) + ggsave(file.path(out_loc,"plots/sctype_exploration",paste0(ind.lib,"_Bcells.png")), + width = 10, height = 15, bg = "white", dpi = 150) +} + +#trying to find which cells in the annotated B from ScType are indeed B cells, by looking at the B cell ScType score +#using the 99 percentile of non-B ScType score as cutoff +plot_Bscore <- function(ind.lib){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + sctype.score <- read.table(file.path(out_loc,"results",paste0(ind.lib,"_sctype_scores.txt")), + sep = "\t", header = T) + Bcell.names <- colnames(seu)[which(seu$sctype_classification=="B")] + + df <- sctype.score[match(Bcell.names, rownames(sctype.score)),] + nonB.df <- df[,which(!colnames(df) %in% "B")] + cutoff <- quantile(unlist(nonB.df, use.names = F), probs = 0.99) #99 percentile of non-B ScType score for annotated B + + seu$B_SctypeScore <- sctype.score$B[match(rownames(sctype.score),colnames(seu))] + p1 <- FeaturePlot(seu, features = "B_SctypeScore") + + scale_color_gradient2(low = "blue", mid = "whitesmoke", high = "red", midpoint = cutoff) + + new.B <- colnames(seu)[which(seu$B_SctypeScore > cutoff)] + new.df <- sctype.score[match(new.B, rownames(sctype.score)),] %>% + tidyr::pivot_longer(cols = colnames(sctype.score), names_to = "celltype", values_to = "ScType.score") + new.df$celltype <- gsub("\\."," ", new.df$celltype) + p2 <- ggplot(new.df, aes(x = ScType.score, y = forcats::fct_reorder(celltype,ScType.score), fill = celltype)) + + geom_density_ridges() + theme_ridges() + + theme(legend.position = "none", axis.title.x = element_text(hjust=0.5), axis.title.y = element_text(vjust=0.5)) + + scale_fill_manual(values = ct_color) + xlab ("ScType score") + + ylab(expr(bold(ScType)*~"("*!!length(new.B)*")")) + p2 + p1 + patchwork::plot_annotation(title = paste0(ind.lib,": new B cells by 99 percentile cutoff of non-B ScType score")) & + theme(plot.title = element_text(hjust = 0.5, face="bold")) + ggsave(file.path(out_loc,"plots/sctype_exploration",paste0(ind.lib,"_newBcells.png")), + width = 10, height = 5, bg = "white", dpi = 150) + + #writing annotation file for normal vs unknown cells + annot.df <- data.frame(FetchData(seu, vars = "sctype_classification")) + annot.df$sctype_classification[match(new.B, rownames(annot.df))] <- "new B" + write.table(annot.df, file = file.path(out_loc,"results",paste0(ind.lib,"_newB-normal-annotation.txt")), + sep = "\t", quote = F, col.names = F) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +dir.create(file.path(out_loc, "plots/sctype_exploration"), showWarnings = FALSE) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +purrr::walk(libraryID, Bcell_check) +purrr::walk(libraryID, plot_Bscore) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/07_run_copykat.R b/analyses/cell-type-ETP-ALL-03/scripts/07_run_copykat.R new file mode 100644 index 000000000..aab0b3f50 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/07_run_copykat.R @@ -0,0 +1,35 @@ +#!/usr/bin/env Rscript + +library(Seurat) +library(copykat) + +run_copykat <- function(ind.lib){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + annot.file <- file.path(out_loc,"results",paste0(ind.lib,"_newB-normal-annotation.txt")) + if (file.exists(annot.file)){ #the sample has new B cells annotated + annot.df <- read.table(annot.file, header=F, row.names=1, sep="\t", stringsAsFactors=FALSE, + colClasses = c('character', 'character')) + norm.cells <- rownames(annot.df)[which(annot.df$V2=="new B")] + n_cores <- parallel::detectCores() - 1 + copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", + ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.lib, + distance="euclidean", norm.cell.names=norm.cells, + output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=n_cores) + idx <- match(colnames(seu), copykat.test$prediction$cell.names) + seu$newB.copykat.pred <- copykat.test$prediction$copykat.pred[idx] + saveRDS(seu, file = file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + } +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +setwd(file.path(out_loc,"results/copykat_output")) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +purrr::walk(libraryID, run_copykat) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/copykat_exploration.R b/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/copykat_exploration.R new file mode 100644 index 000000000..971c0c22e --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/copykat_exploration.R @@ -0,0 +1,63 @@ +#!/usr/bin/env Rscript + +#This script explores the results of CopyKat prediction with respective of cell types and blast module scores + +library(Seurat) +library(ggplot2) +library(dplyr) + +copykatInterpret <- function(annot.obj, library.id, ct.colors){ + tryCatch({ + exprs <- data.frame(FetchData(annot.obj, vars = c("sctype_classification","newB.copykat.pred","lowConfidence_annot"))) + df <- exprs %>% + group_by(newB.copykat.pred, sctype_classification) %>% + count(name = "Frequency") + total_df <- df %>% + group_by(newB.copykat.pred) %>% + summarise(Total = sum(Frequency)) + p1 <- ggplot() + + geom_bar(data = df, aes(x = newB.copykat.pred, y = Frequency, fill = sctype_classification), width = 0.5, stat = "identity", position = "fill") + + geom_text(data = total_df, aes(y = 100, x = newB.copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + + scale_fill_manual(values = ct_color) + + df <- exprs %>% + dplyr::group_by(newB.copykat.pred, lowConfidence_annot) %>% + dplyr::count(name = "Frequency") + p2 <- ggplot() + + geom_bar(data = df, aes(x = newB.copykat.pred, y = Frequency, fill = lowConfidence_annot), width = 0.5, stat = "identity", position = "fill") + + geom_text(data = total_df, aes(y = 100, x = newB.copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + + scale_fill_manual(values = ct_color) + cowplot::plot_grid(plotlist = list(p1,p2), nrow = 1) + + cowplot::draw_figure_label(library.id, position = "top", size = 14, fontface = "bold") + ggsave(file.path(out_loc,"plots/copykat_exploration",paste0(library.id,"_celltypeVScopykat.png")), width = 10, height = 5, bg = "white", dpi = 150) + + ### plotting blast module scores + Idents(annot.obj) <- factor(annot.obj$newB.copykat.pred, levels = c("aneuploid","diploid","not.defined")) + VlnPlot(annot.obj, features = "Blast_Features1") + ggtitle(paste0(library.id,": Blast module score")) + NoLegend() + ggsave(file.path(out_loc,"plots/copykat_exploration",paste0(library.id,"_blastModuleScore.png")), width = 6, height = 6, bg = "white", dpi = 150) + }, error=function(e){}) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +dir.create(file.path(out_loc, "plots/copykat_exploration"), showWarnings = FALSE) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +seu.list <- list() +for (lib_iter in 1:length(libraryID)){ + seu.list[[lib_iter]] <- readRDS(file.path(out_loc,"results/rds",paste0(libraryID[lib_iter],".rds"))) + names(seu.list)[lib_iter] <- libraryID[lib_iter] +} + +purrr::walk2(seu.list, names(seu.list), ~ copykatInterpret (annot.obj = .x, library.id = .y, ct.colors = ct_color)) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/run_infercnv.R b/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/run_infercnv.R new file mode 100644 index 000000000..a62282c77 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/exploratory_analyses/run_infercnv.R @@ -0,0 +1,51 @@ +#!/usr/bin/env Rscript + +#This script runs inferCNV using the new B cells, and trying to identify "non-malignant" group based on the infercnv.png + +library(Seurat) + +run_inferCNV <- function(ind.lib){ + dir.create(file.path(scratch_dir, ind.lib), showWarnings = FALSE) + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + annot.file <- file.path(out_loc,"results",paste0(ind.lib,"_newB-normal-annotation.txt")) + infercnv_obj <- infercnv::CreateInfercnvObject(raw_counts_matrix=seu@assays[["RNA"]]@counts, + annotations_file=annot.file, + delim="\t", + gene_order_file=geneFile, + ref_group_names="new B") + options(scipen = 100) + infercnv_obj <- infercnv::run(infercnv_obj, + cutoff = 0.1, # use 1 for smart-seq, 0.1 for 10x-genomics + out_dir = file.path(scratch_dir,ind.lib), # save all intermediate files to scratch dir + denoise = T, HMM = T, analysis_mode = "samples", + save_rds = F, # don't save the intermediate rds files + num_threads = parallel::detectCores() - 1) + + # create table with barcodes and CNVs for each chromosome + infercnv::add_to_seurat( + seurat_obj = NULL, + infercnv_output_path = file.path(scratch_dir, ind.lib)) + + ### adding clusterID from the cutting of hierarchical clustering to seu object + final_cnv_obj <- readRDS(file.path(out_loc, "results/infercnv_output", paste0(ind.lib,"_run.final.infercnv_obj"))) + hres <- final_cnv_obj@tumor_subclusters$hc$all_observations + obs.clusID <- cutree(hres,4) #4 clusters for SCPCL000703 + hres <- dendextend::color_branches(hres, k= 4) + plot(hres) + seu$infercnv.pred <- obs.clusID[match(names(obs.clusID), colnames(seu))] +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +scratch_dir <- file.path(out_loc,"scratch") + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +geneFile <- file.path(project_root, "references/infercnv_refs/Homo_sapiens.GRCh38.104.gene_order.txt") + +purrr::walk(libraryID, run_inferCNV) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/markerGenes_submission.R b/analyses/cell-type-ETP-ALL-03/scripts/markerGenes_submission.R new file mode 100644 index 000000000..bae557899 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/markerGenes_submission.R @@ -0,0 +1,31 @@ +#!/usr/bin/env Rscript + +#This script converts the "Azimuth_BM_level1.csv" into the submission format for marker genes + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") + +all_sources <- c("https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow", + "doi:10.1038/s41598-023-39152-z","https://sctype.app/database.php") +gene.df <- read.table(file.path(out_loc,"Azimuth_BM_level1.csv"), sep = ",", header = T) + +df1 <- c() +for (i in 1:nrow(gene.df)){ + tmp.gene <- strsplit(gene.df$ensembl_id_positive_marker[i],",")[[1]] + if (gene.df$cellName[i] == "Blast"){ + source <- all_sources[2] + }else if (gene.df$cellName[i] %in% c("Pre Eryth","Cancer")){ + source <- all_sources[3] + }else{source <- all_sources[1]} + + df1 <- rbind(df1,data.frame(ensembl_gene_id=tmp.gene, + cell_type=rep(gene.df$cellName[i],length(tmp.gene)), + source=rep(source,length(tmp.gene)))) +} + +df2 <- by(df1, df1$ensembl_gene_id, \(x) list2DF(lapply(x, \(.) toString(unique(.))))) |> + do.call(what=rbind) + +write.table(df2, file = file.path(out_loc,"submission_markerGenes.tsv"), sep = "\t", + row.names = F, quote = F) diff --git a/analyses/cell-type-ETP-ALL-03/scripts/util/gene-set-functions.R b/analyses/cell-type-ETP-ALL-03/scripts/util/gene-set-functions.R new file mode 100644 index 000000000..2e19d4085 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/util/gene-set-functions.R @@ -0,0 +1,45 @@ +#!/usr/bin/env Rscript + +#This script prepares the gene set for each cell type, extracting them from the marker list + +gene_sets_prepare <- function(path_to_db_file, cell_type){ + cell_markers = read.csv(path_to_db_file, header = T) + cell_markers = cell_markers[cell_markers$tissueType == cell_type,] + cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker); cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) + + # correct gene symbols from the given DB (up-genes) + cell_markers$ensembl_id_positive_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_positive_marker[i],","))) + markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) + markers_all = sort(markers_all) + + if(length(markers_all) > 0){ + suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here + paste0(markers_all, collapse=",") + } else { + "" + } + }) + + # correct gene symbols from the given DB (down-genes) + cell_markers$ensembl_id_negative_marker = sapply(1:nrow(cell_markers), function(i){ + markers_all = gsub(" ", "", unlist(strsplit(cell_markers$ensembl_id_negative_marker[i],","))) + markers_all = toupper(markers_all[markers_all != "NA" & markers_all != ""]) + markers_all = sort(markers_all) + + if(length(markers_all) > 0){ + suppressMessages({markers_all = unique(na.omit(markers_all))}) #since the markers are provided in Ensembl ID, I removed checkGeneSymbols function here + paste0(markers_all, collapse=",") + } else { + "" + } + }) + + cell_markers$ensembl_id_positive_marker = gsub("///",",",cell_markers$ensembl_id_positive_marker);cell_markers$ensembl_id_positive_marker = gsub(" ","",cell_markers$ensembl_id_positive_marker) + cell_markers$ensembl_id_negative_marker = gsub("///",",",cell_markers$ensembl_id_negative_marker);cell_markers$ensembl_id_negative_marker = gsub(" ","",cell_markers$ensembl_id_negative_marker) + + gs = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_positive_marker[j]),",")))); names(gs) = cell_markers$cellName + gs2 = lapply(1:nrow(cell_markers), function(j) gsub(" ","",unlist(strsplit(toString(cell_markers$ensembl_id_negative_marker[j]),",")))); names(gs2) = cell_markers$cellName + + list(gs_positive = gs, gs_negative = gs2) +} \ No newline at end of file diff --git a/analyses/cell-type-ETP-ALL-03/scripts/writeout_submission.R b/analyses/cell-type-ETP-ALL-03/scripts/writeout_submission.R new file mode 100644 index 000000000..ecbc276d1 --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/scripts/writeout_submission.R @@ -0,0 +1,59 @@ +#!/usr/bin/env Rscript + +library(Seurat) +library(ggplot2) + +writeout <- function(ind.lib, sample.ID, ct.colors, n.row = 1){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + voi <- c('tumor_cell_classification','cell_type_assignment') + tumor_classification <- c('tumor','normal','unknown') + names(tumor_classification) <- c('aneuploid','diploid','not.defined') + seu$cell_type_assignment <- seu$sctype_classification + tryCatch({ + seu$tumor_cell_classification <- as.vector(tumor_classification[match(seu$newB.copykat.pred,names(tumor_classification))]) + }, error=function(e){}) + tryCatch({ + voi_df <- data.frame(FetchData(seu, vars = voi)) |> tibble::rownames_to_column(var = "cell_barcode") + }, error=function(e){}) + final.df <- data.frame(scpca_sample_id=rep(sample.ID, nrow(voi_df)), voi_df, + CL_ontology_id=gene.df$ontologyID[match(voi_df$cell_type_assignment,gene.df$cellName)]) + write.table(final.df, sep = "\t", quote = F, row.names = F, + file = file.path(out_loc,"results/submission_table",paste0(ind.lib,"_metadata.tsv"))) + + ## plotting the variables + plot.list <- list() + for (plot.type in voi){ + if (plot.type == "cell_type_assignment"){ + clrs <- ct.colors + } else{ + clrs <- NULL + } + tryCatch({ + plot.list[[plot.type]] <- DimPlot(seu, reduction = "Xumap_", group.by = plot.type, + label = T, cols = clrs, repel = T) + }, error=function(e){}) + } + cowplot::plot_grid(plotlist = plot.list, nrow = n.row) + patchwork::plot_annotation(title = ind.lib) & + theme(plot.title = element_text(hjust = 0.5, size = 18, face="bold")) + ggsave(file.path(out_loc,"results/submission_table",paste0("multipanels_",ind.lib,".png")), width = 12, height = 5, bg = "white", dpi = 150) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-ETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +dir.create(file.path(out_loc, "results/submission_table"), showWarnings = FALSE) + +gene.df <- read.table(file.path(out_loc, "Azimuth_BM_level1.csv"), sep = ",", header = T) +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id +sampleID <- metadata$scpca_sample_id + +purrr::walk2(libraryID, sampleID, ~ writeout(ind.lib = .x, sample.ID = .y, ct.colors = ct_color)) diff --git a/analyses/cell-type-ETP-ALL-03/submission_markerGenes.tsv b/analyses/cell-type-ETP-ALL-03/submission_markerGenes.tsv new file mode 100644 index 000000000..bf66d938e --- /dev/null +++ b/analyses/cell-type-ETP-ALL-03/submission_markerGenes.tsv @@ -0,0 +1,152 @@ +ensembl_gene_id cell_type source +ENSG00000002586 Blast doi:10.1038/s41598-023-39152-z +ENSG00000005961 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000007062 Cancer https://sctype.app/database.php +ENSG00000011465 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000012124 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000014914 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000026508 Cancer https://sctype.app/database.php +ENSG00000041982 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000047457 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000048462 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000069667 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000070031 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000072274 Pre Eryth https://sctype.app/database.php +ENSG00000073754 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000075340 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000075618 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000081059 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000081237 Pre Eryth https://sctype.app/database.php +ENSG00000086205 Cancer https://sctype.app/database.php +ENSG00000088726 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000091513 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000100385 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000100450 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000101200 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000102145 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104660 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104894 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104974 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105251 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105369 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105610 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000106327 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000107742 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000110195 Pre Eryth https://sctype.app/database.php +ENSG00000110203 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000111057 Cancer https://sctype.app/database.php +ENSG00000111796 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112077 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112175 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112212 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000113088 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000113140 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115232 Pre Eryth https://sctype.app/database.php +ENSG00000115461 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115607 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115718 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115884 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000116191 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000117281 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000117632 Blast doi:10.1038/s41598-023-39152-z +ENSG00000119865 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000119888 HSPC, Late Eryth, Cancer https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow, https://sctype.app/database.php +ENSG00000121769 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000123416 Blast doi:10.1038/s41598-023-39152-z +ENSG00000124491 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000124766 Blast doi:10.1038/s41598-023-39152-z +ENSG00000125810 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000130203 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000130208 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000131016 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000132514 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000132704 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000133742 Pre Eryth https://sctype.app/database.php +ENSG00000133789 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000134545 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000135218 Pre Eryth https://sctype.app/database.php +ENSG00000135525 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000138639 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000138795 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000139193 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000139329 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000141736 Cancer https://sctype.app/database.php +ENSG00000143184 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000143297 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000144290 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000145220 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000147571 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150045 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150681 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150687 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000152518 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000152583 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153064 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153071 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153563 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000155367 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000156738 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000156966 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000158825 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000159189 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000160307 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000160654 CD4 T, CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000162444 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163534 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163554 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163687 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163736 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163737 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000164692 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000166211 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000166523 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000167286 CD4 T, CD8 T, Blast https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow, doi:10.1038/s41598-023-39152-z +ENSG00000167476 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000168685 CD4 T, Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000168913 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000169432 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000169704 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000170180 Pre Eryth https://sctype.app/database.php +ENSG00000170323 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000170891 HSPC, Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000171051 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172005 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172116 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172247 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172995 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173369 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173372 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173762 Blast doi:10.1038/s41598-023-39152-z +ENSG00000175792 Pre Eryth https://sctype.app/database.php +ENSG00000176783 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000177606 Blast doi:10.1038/s41598-023-39152-z +ENSG00000178573 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000179348 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000184613 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000186074 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000186710 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000187699 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000188672 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000189430 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000196188 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000197353 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198178 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198574 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198851 CD4 T, CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000204010 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211640 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211673 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211685 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000213934 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000215788 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000222037 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000223609 Pre Eryth https://sctype.app/database.php +ENSG00000227191 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000227507 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000239961 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000240505 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000243466 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000244734 Pre Eryth https://sctype.app/database.php +ENSG00000250722 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000271503 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000275385 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow diff --git a/analyses/cell-type-ewings/renv.lock b/analyses/cell-type-ewings/renv.lock index 2561148d5..4693d301a 100644 --- a/analyses/cell-type-ewings/renv.lock +++ b/analyses/cell-type-ewings/renv.lock @@ -3843,6 +3843,29 @@ ], "Hash": "16f095487215acf101cdc3ed3da237cf" }, + "rOpenScPCA": { + "Package": "rOpenScPCA", + "Version": "0.1.0", + "Source": "GitHub", + "RemoteType": "github", + "RemoteHost": "api.github.com", + "RemoteUsername": "AlexsLemonade", + "RemoteRepo": "rOpenScPCA", + "RemoteRef": "main", + "RemoteSha": "fe1cf9f9ad016a1e9724a6274e88a5b841455bf5", + "Requirements": [ + "BiocParallel", + "SingleCellExperiment", + "bluster", + "dplyr", + "methods", + "pdfCluster", + "purrr", + "tibble", + "tidyr" + ], + "Hash": "b6805f308a847b08322572b63da51a4d" + }, "rappdirs": { "Package": "rappdirs", "Version": "0.3.3", @@ -4173,8 +4196,8 @@ "Package": "scpcaTools", "Version": "0.3.2", "Source": "GitHub", - "Remotes": "AlexsLemonade/scpcaData, immunogenomics/lisi@v1.0", "RemoteType": "github", + "Remotes": "AlexsLemonade/scpcaData, immunogenomics/lisi@v1.0", "RemoteHost": "api.github.com", "RemoteRepo": "scpcaTools", "RemoteUsername": "AlexsLemonade", diff --git a/analyses/cell-type-nonETP-ALL-03/Dockerfile b/analyses/cell-type-nonETP-ALL-03/Dockerfile index dffa86a9b..ce6ff54bb 100644 --- a/analyses/cell-type-nonETP-ALL-03/Dockerfile +++ b/analyses/cell-type-nonETP-ALL-03/Dockerfile @@ -52,6 +52,9 @@ RUN conda-lock install -n ${ENV_NAME} conda-lock.yml \ # Copy the renv.lock file from the host environment to the image COPY renv.lock renv.lock +# Temporarily install Rhtslib separately +RUN Rscript -e 'BiocManager::install("Rhtslib")' + # restore from renv.lock file and clean up to reduce image size RUN Rscript -e 'renv::restore()' \ && rm -rf ~/.cache/R/renv \ diff --git a/analyses/cell-type-nonETP-ALL-03/README.md b/analyses/cell-type-nonETP-ALL-03/README.md index cc6045588..e3502fec3 100644 --- a/analyses/cell-type-nonETP-ALL-03/README.md +++ b/analyses/cell-type-nonETP-ALL-03/README.md @@ -8,9 +8,11 @@ We first aim to annotate the cell types in non-ETP T-ALL, and use the annotated - We use the cell type marker (`Azimuth_BM_level1.csv`) from [Azimuth Human Bone Marrow reference](https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow). In total, there are 14 cell types: B, CD4T, CD8T, Other T, DC, Monocytes, Macrophages, NK, Early Erythrocytes, Late Erythrocytes, Plasma, Platelet, Stromal, and Hematopoietic Stem and Progenitor Cells (HSPC). Based on the exploratory analysis, we believe that most of the cells in these samples do not express adequate markers to be distinguished at finer cell type level (eg. naive vs memory, CD14 vs CD16 etc.), and majority of the cells should belong to T-cells. In addition, we include the marker genes for blast cell [[Bhasin et al. (2023)](https://www.nature.com/articles/s41598-023-39152-z)] as well as erythroid precursor and cancer cell in immune system [[ScType](https://sctype.app/database.php) database]. + \*\*`Azimuth_BM_level1.csv` is converted to `submission_markerGenes.tsv`, in the final submission format. + - Since ScType annotates cell types at cluster level using marker genes provided by user or from the built-in database, we employ [self-assembling manifold](https://github.com/atarashansky/self-assembling-manifold/tree/master) (SAM) algorithm, a soft feature selection strategy for better separation of homogeneous cell types. -- After cell type annotation, we provide B cells as the normal cells in the sample, if there is any, to [CopyKat](https://github.com/navinlabcode/copykat), for identification of tumor cells. +- After cell type annotation, we fine-tune the annotated B cells by applying 99 percentile cutoff of non-B ScType score on the "B cell clusters". We then use the new B cells (i.e those cells which passed the cutoff) as the normal cells in running [CopyKat](https://github.com/navinlabcode/copykat), for the identification of tumor cells. We could not detect strong B cell signal in `SCPCL000082`. Here are the steps in the module: @@ -18,6 +20,10 @@ Here are the steps in the module: 2. Annotating cell type using ScType and identifying tumor cells using CopyKat (`scripts/02-03_annotation.R`) +3. Fine-tuning the B cells (`scripts/06_sctype_exploration.R`) + +4. Re-running CopyKat (`scripts/07_run_copykat.R`) + ## Usage Before running Rscripts in R or Rstudio, we first need to prepare the input files as shown in the next section, and run the following codes in the terminal for installing required libraries: @@ -44,21 +50,15 @@ The `scripts/00-01_processing_rds.R` requires the processed SingleCellExperiment As for the annotation, `scripts/02-03_annotation.R` requires cell type marker gene file, `Azimuth_BM_level1.csv`, as an input for ScType. This excel file contains a list of positive marker genes in Ensembl ID under `ensembl_id_positive_marker` for each cell type; *TMEM56* and *CD235a* are not detected in our dataset, thus they are being removed as part of the markers for Late Eryth and Pre Eryth respectively. As of now, there is no negative marker genes provided under `ensembl_id_negative_marker`. -## Output files - -Running `scripts/00-01_processing_rds.R` will generate two types of output: - -- `rds` objects in `scratch/` - -- umap plots showing leiden clustering in `plots/` +## Important output files -Running `scripts/02-03_annotation.R` will generate several outputs: +- `rds` objects in `results/rds` -- updated `rds` objects in `scratch/` +- ScType results of top 10 possible cell types in a cluster (`results/_sctype_top10_celltypes_perCluster.txt`) and ScType score (`results/_sctype_scores.txt`) -- umap plots showing cell type and CopyKat prediction (if there is any) and dotplots showing the features added with `AddModuleScore()` in `plots/` +- location of fine-tuned B cells in umap (`plots/sctype_exploration/_newBcells.png`) and the cell type assignment with added fine-tuned B cells (`results/_newB-normal-annotation.txt`) -- ScType results of top 10 possible cell types in a cluster (`_sctype_top10_celltypes_perCluster.txt`) and metadata file tabulating leiden cluster, cell type, low confidence cell type, and CopyKat prediction for each cell (`_metadata.txt`) in `results/` +- final submission table (`results/submission_table/_metadata.tsv`) and the umap plots showing cell_type_assignment from ScType and tumor_cell_classification from CopyKat using fine-tuned B cells (`results/submission_table/multipanels_.png`) ## Software requirements diff --git a/analyses/cell-type-nonETP-ALL-03/plots/copykat_exploration/SCPCL000076_blastModuleScore.png b/analyses/cell-type-nonETP-ALL-03/plots/copykat_exploration/SCPCL000076_blastModuleScore.png index c17dd4f95..16ef89395 100644 Binary files a/analyses/cell-type-nonETP-ALL-03/plots/copykat_exploration/SCPCL000076_blastModuleScore.png and b/analyses/cell-type-nonETP-ALL-03/plots/copykat_exploration/SCPCL000076_blastModuleScore.png differ diff --git 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b/analyses/cell-type-nonETP-ALL-03/plots/merging/multipanels_SCPCL000710-SCPCL000703.png similarity index 100% rename from analyses/cell-type-nonETP-ALL-03/plots/multipanels_SCPCL000710-SCPCL000703.png rename to analyses/cell-type-nonETP-ALL-03/plots/merging/multipanels_SCPCL000710-SCPCL000703.png diff --git a/analyses/cell-type-nonETP-ALL-03/renv.lock b/analyses/cell-type-nonETP-ALL-03/renv.lock index 708c6c33b..b2fcd483d 100644 --- a/analyses/cell-type-nonETP-ALL-03/renv.lock +++ b/analyses/cell-type-nonETP-ALL-03/renv.lock @@ -66,6 +66,20 @@ ], "Hash": "ef32d07aafdd12f24c5827374ae3590d" }, + "BiocIO": { + "Package": "BiocIO", + "Version": "1.14.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "R", + "S4Vectors", + "methods", + "tools" + ], + "Hash": "f97a7ef01d364cf20d1946d43a3d526f" + }, "BiocManager": { "Package": "BiocManager", "Version": "1.30.25", @@ -121,6 +135,26 @@ ], "Hash": "b892e27fc9659a4c8f8787d34c37b8b2" }, + "Biostrings": { + "Package": "Biostrings", + "Version": "2.72.1", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "GenomeInfoDb", + "IRanges", + "R", + "S4Vectors", + "XVector", + "crayon", + "grDevices", + "methods", + "stats", + "utils" + ], + "Hash": "886ff0ed958d6f839ed2e0d01f6853b3" + }, "DelayedArray": { "Package": "DelayedArray", "Version": "0.30.1", @@ -143,13 +177,13 @@ }, "FNN": { "Package": "FNN", - "Version": "1.1.4", + "Version": "1.1.4.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "R" ], - "Hash": "eaabdc7938aa3632a28273f53a0d226d" + "Hash": "c7b4a49c7f435d0a67bb1e127e953d75" }, "GenomeInfoDb": { "Package": "GenomeInfoDb", @@ -179,9 +213,31 @@ ], "Hash": "c3c792a7b7f2677be56e8632c5b7543d" }, + "GenomicAlignments": { + "Package": "GenomicAlignments", + "Version": "1.40.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + 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+ "stats", + "stats4", + "survival", + "utils" + ], + "Hash": "4084b5070a40ad99dad581ed3b67bd55" + }, "colorspace": { "Package": "colorspace", "Version": "2.1-1", @@ -884,10 +1105,10 @@ }, "commonmark": { "Package": "commonmark", - "Version": "1.9.1", + "Version": "1.9.2", "Source": "Repository", - "Repository": "CRAN", - "Hash": "5d8225445acb167abf7797de48b2ee3c" + "Repository": "RSPM", + "Hash": "14eb0596f987c71535d07c3aff814742" }, "copykat": { "Package": "copykat", @@ -897,11 +1118,9 @@ "RemoteHost": "api.github.com", "RemoteRepo": "copykat", "RemoteUsername": "navinlabcode", - "RemoteRef": "HEAD", "RemoteSha": "d7d6569ae9e30bf774908301af312f626de4cbd5", "Requirements": [ "MCMCpack", - "R", "RColorBrewer", "cluster", "dlm", @@ -910,7 +1129,7 @@ "parallel", "parallelDist" ], - "Hash": "e356046a6ab19635791f7ce46ecd5991" + "Hash": "efd05c69dffe1128eb4843f3107eb606" }, "cowplot": { "Package": "cowplot", @@ -966,24 +1185,24 @@ }, "curl": { "Package": "curl", - "Version": "5.2.2", + 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"Hash": "e0b3a53876554bd45879e596cdb10a52" + "Hash": "5899f1eaa825580172bb56c08266f37c" }, "goftest": { "Package": "goftest", @@ -1363,9 +1669,9 @@ }, "gplots": { "Package": "gplots", - "Version": "3.1.3.1", + "Version": "3.2.0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "KernSmooth", "R", @@ -1374,7 +1680,7 @@ "methods", "stats" ], - "Hash": "f72b5d1ed587f8905e38ee7295e88d80" + "Hash": "d24febf39c58dcb5a6cc6a12bd66d40a" }, "gridExtra": { "Package": "gridExtra", @@ -1459,6 +1765,20 @@ ], "Hash": "d65ba49117ca223614f71b60d85b8ab7" }, + "hms": { + "Package": "hms", + "Version": "1.1.3", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "lifecycle", + "methods", + "pkgconfig", + "rlang", + "vctrs" + ], + "Hash": "b59377caa7ed00fa41808342002138f9" + }, "htmltools": { "Package": "htmltools", "Version": "0.5.8.1", @@ -1529,9 +1849,9 @@ }, "igraph": { "Package": "igraph", - "Version": "2.0.3", + "Version": "2.1.1", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "Matrix", "R", @@ -1548,7 +1868,54 @@ "utils", "vctrs" ], - "Hash": "c3b7d801d722e26e4cd888e042bf9af5" + "Hash": "c03878b48737a0e2da3b772d7b2e22da" + }, + "infercnv": { + "Package": "infercnv", + "Version": "1.20.0", + "Source": "Bioconductor", + "Repository": "Bioconductor 3.19", + "Requirements": [ + "BiocGenerics", + "HiddenMarkov", + "Matrix", + "R", + "RANN", + "RColorBrewer", + "Seurat", + "SingleCellExperiment", + "SummarizedExperiment", + "ape", + "argparse", + "caTools", + "coda", + "coin", + "digest", + "doParallel", + "dplyr", + "edgeR", + "fastcluster", + "fitdistrplus", + "foreach", + "futile.logger", + "future", + "ggplot2", + "gplots", + "grDevices", + "graphics", + "gridExtra", + "igraph", + "methods", + "parallel", + "parallelDist", + "phyclust", + "reshape2", + "rjags", + "stats", + "tidyr", + "utils" + ], + "Hash": "eaf21e1aab39c0b466304b316cc8d031" }, "irlba": { "Package": 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+ }, + "sandwich": { + "Package": "sandwich", + "Version": "3.1-1", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "stats", + "utils", + "zoo" + ], + "Hash": "072bb2d27425f2a58fe71fe1080676ce" + }, "sass": { "Package": "sass", "Version": "0.4.9", @@ -2312,7 +2914,6 @@ "RemoteHost": "api.github.com", "RemoteRepo": "schard", "RemoteUsername": "cellgeni", - "RemoteRef": "HEAD", "RemoteSha": "ac382a6bdfc0fff164064f8dd0f0c3e29d0a15fc", "Requirements": [ "Matrix", @@ -2440,9 +3041,9 @@ }, "spam": { "Package": "spam", - "Version": "2.10-0", + "Version": "2.11-0", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "R", "Rcpp", @@ -2450,7 +3051,7 @@ "grid", "methods" ], - "Hash": "ffe1f9e95a4375530747b268f82b5086" + "Hash": "6581c0a0bb8594b85c8de869644174ac" }, "spatstat.data": { "Package": "spatstat.data", @@ -2582,6 +3183,18 @@ ], "Hash": "69b26ceb9f7976f347049b4d470c2d65" }, + "statmod": { + "Package": "statmod", + "Version": "1.5.0", + "Source": "Repository", + "Repository": "RSPM", + "Requirements": [ + "R", + "graphics", + "stats" + ], + "Hash": "26e158d12052c279bdd4ba858b80fb1f" + }, "stringi": { "Package": "stringi", "Version": "1.8.4", @@ -2630,10 +3243,10 @@ }, "sys": { "Package": "sys", - "Version": "3.4.2", + "Version": "3.4.3", "Source": "Repository", - "Repository": "CRAN", - "Hash": "3a1be13d68d47a8cd0bfd74739ca1555" + "Repository": "RSPM", + "Hash": "de342ebfebdbf40477d0758d05426646" }, "tensor": { "Package": "tensor", @@ -2710,6 +3323,17 @@ ], "Hash": "9db859e8aabbb474293dde3097839420" }, + "tzdb": { + "Package": "tzdb", + "Version": "0.4.0", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "cpp11" + ], + "Hash": "f561504ec2897f4d46f0c7657e488ae1" + }, "utf8": { "Package": "utf8", "Version": "1.2.4", @@ -2752,6 +3376,19 @@ ], "Hash": "c03fa420630029418f7e6da3667aac4a" }, + "viridis": { + "Package": "viridis", + "Version": "0.6.5", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "ggplot2", + "gridExtra", + "viridisLite" + ], + "Hash": "acd96d9fa70adeea4a5a1150609b9745" + }, "viridisLite": { "Package": "viridisLite", "Version": "0.4.2", @@ -2762,6 +3399,32 @@ ], "Hash": "c826c7c4241b6fc89ff55aaea3fa7491" }, + "vroom": { + "Package": "vroom", + "Version": "1.6.5", + "Source": "Repository", + "Repository": "CRAN", + "Requirements": [ + "R", + "bit64", + "cli", + "cpp11", + "crayon", + "glue", + "hms", + "lifecycle", + "methods", + "progress", + "rlang", + "stats", + "tibble", + "tidyselect", + "tzdb", + "vctrs", + "withr" + ], + "Hash": "390f9315bc0025be03012054103d227c" + }, "withr": { "Package": "withr", "Version": "3.0.1", @@ -2776,16 +3439,16 @@ }, "xfun": { "Package": "xfun", - "Version": "0.47", + "Version": "0.48", "Source": "Repository", - "Repository": "CRAN", + "Repository": "RSPM", "Requirements": [ "R", "grDevices", "stats", "tools" ], - "Hash": "36ab21660e2d095fef0d83f689e0477c" + "Hash": "89e455b87c84e227eb7f60a1b4e5fe1f" }, "xtable": { "Package": "xtable", diff --git a/analyses/cell-type-nonETP-ALL-03/renv/activate.R b/analyses/cell-type-nonETP-ALL-03/renv/activate.R index d13f9932a..0eb51088a 100644 --- a/analyses/cell-type-nonETP-ALL-03/renv/activate.R +++ b/analyses/cell-type-nonETP-ALL-03/renv/activate.R @@ -2,7 +2,7 @@ local({ # the requested version of renv - version <- "1.0.7" + version <- "1.0.11" attr(version, "sha") <- NULL # the project directory @@ -98,6 +98,66 @@ local({ unloadNamespace("renv") # load bootstrap tools + ansify <- function(text) { + if (renv_ansify_enabled()) + renv_ansify_enhanced(text) + else + renv_ansify_default(text) + } + + renv_ansify_enabled <- function() { + + override <- Sys.getenv("RENV_ANSIFY_ENABLED", unset = NA) + if (!is.na(override)) + return(as.logical(override)) + + pane <- Sys.getenv("RSTUDIO_CHILD_PROCESS_PANE", unset = NA) + if (identical(pane, "build")) + return(FALSE) + + testthat <- Sys.getenv("TESTTHAT", unset = "false") + if (tolower(testthat) %in% "true") + return(FALSE) + + iderun <- Sys.getenv("R_CLI_HAS_HYPERLINK_IDE_RUN", unset = "false") + if (tolower(iderun) %in% "false") + return(FALSE) + + TRUE + + } + + renv_ansify_default <- function(text) { + text + } + + renv_ansify_enhanced <- function(text) { + + # R help links + pattern <- "`\\?(renv::(?:[^`])+)`" + replacement <- "`\033]8;;ide:help:\\1\a?\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # runnable code + pattern <- "`(renv::(?:[^`])+)`" + replacement <- "`\033]8;;ide:run:\\1\a\\1\033]8;;\a`" + text <- gsub(pattern, replacement, text, perl = TRUE) + + # return ansified text + text + + } + + renv_ansify_init <- function() { + + envir <- renv_envir_self() + if (renv_ansify_enabled()) + assign("ansify", renv_ansify_enhanced, envir = envir) + else + assign("ansify", renv_ansify_default, envir = envir) + + } + `%||%` <- function(x, y) { if (is.null(x)) y else x } @@ -142,7 +202,10 @@ local({ # compute common indent indent <- regexpr("[^[:space:]]", lines) common <- min(setdiff(indent, -1L)) - leave - paste(substring(lines, common), collapse = "\n") + text <- paste(substring(lines, common), collapse = "\n") + + # substitute in ANSI links for executable renv code + ansify(text) } @@ -305,8 +368,11 @@ local({ quiet = TRUE ) - if ("headers" %in% names(formals(utils::download.file))) - args$headers <- renv_bootstrap_download_custom_headers(url) + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(url) + if (length(headers) && is.character(headers)) + args$headers <- headers + } do.call(utils::download.file, args) @@ -385,10 +451,21 @@ local({ for (type in types) { for (repos in renv_bootstrap_repos()) { + # build arguments for utils::available.packages() call + args <- list(type = type, repos = repos) + + # add custom headers if available -- note that + # utils::available.packages() will pass this to download.file() + if ("headers" %in% names(formals(utils::download.file))) { + headers <- renv_bootstrap_download_custom_headers(repos) + if (length(headers) && is.character(headers)) + args$headers <- headers + } + # retrieve package database db <- tryCatch( as.data.frame( - utils::available.packages(type = type, repos = repos), + do.call(utils::available.packages, args), stringsAsFactors = FALSE ), error = identity @@ -470,6 +547,14 @@ local({ } + renv_bootstrap_github_token <- function() { + for (envvar in c("GITHUB_TOKEN", "GITHUB_PAT", "GH_TOKEN")) { + envval <- Sys.getenv(envvar, unset = NA) + if (!is.na(envval)) + return(envval) + } + } + renv_bootstrap_download_github <- function(version) { enabled <- Sys.getenv("RENV_BOOTSTRAP_FROM_GITHUB", unset = "TRUE") @@ -477,16 +562,16 @@ local({ return(FALSE) # prepare download options - pat <- Sys.getenv("GITHUB_PAT") - if (nzchar(Sys.which("curl")) && nzchar(pat)) { + token <- renv_bootstrap_github_token() + if (nzchar(Sys.which("curl")) && nzchar(token)) { fmt <- "--location --fail --header \"Authorization: token %s\"" - extra <- sprintf(fmt, pat) + extra <- sprintf(fmt, token) saved <- options("download.file.method", "download.file.extra") options(download.file.method = "curl", download.file.extra = extra) on.exit(do.call(base::options, saved), add = TRUE) - } else if (nzchar(Sys.which("wget")) && nzchar(pat)) { + } else if (nzchar(Sys.which("wget")) && nzchar(token)) { fmt <- "--header=\"Authorization: token %s\"" - extra <- sprintf(fmt, pat) + extra <- sprintf(fmt, token) saved <- options("download.file.method", "download.file.extra") options(download.file.method = "wget", download.file.extra = extra) on.exit(do.call(base::options, saved), add = TRUE) diff --git a/analyses/cell-type-nonETP-ALL-03/results/README.md b/analyses/cell-type-nonETP-ALL-03/results/README.md index 11080bc8d..0c0fc2baa 100644 --- a/analyses/cell-type-nonETP-ALL-03/results/README.md +++ b/analyses/cell-type-nonETP-ALL-03/results/README.md @@ -12,8 +12,12 @@ These are the generated outputs for each sample in the S3 bucket: - `rds` objects: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/rds` - metadata and ScType results: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/` - CopyKat results: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/copykat_output` +- InferCNV results: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/infercnv_output` - evaluating cluster separation, stability, and purity: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/evalClus` - umap and dot plots: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots` - violin and stacked bar plots for exploring the results of CopyKat prediction: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots/copykat_exploration` +- ridge plots showing the ScType score for each cell type in annotated B cells from ScType, SingleR, and CellAssign, as well as the scatter plots showing the relationship between B cell ScType score and cluster purity of these cells: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots/sctype_exploration` +- ridge plots showing the ScType score for each cell type in fine-tuned B cells and feature plots showing the distribution of B cell ScType score: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/plots/sctype_exploration` +- final submission `tsv` files and `png` for cell type and/or tumor cell classification: `s3://researcher-650251722463-us-east-2/cell-type-nonETP-ALL-03/results/submission_table` \*\*All the plots are also found in the repository plots/. diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/05_cluster_evaluation.R b/analyses/cell-type-nonETP-ALL-03/scripts/05_cluster_evaluation.R index 11c31a42b..f99b175cc 100644 --- a/analyses/cell-type-nonETP-ALL-03/scripts/05_cluster_evaluation.R +++ b/analyses/cell-type-nonETP-ALL-03/scripts/05_cluster_evaluation.R @@ -1,47 +1,55 @@ #!/usr/bin/env Rscript -## Calculates silhouette score and purity for each cluster and evalutes their stability, +## Calculates silhouette score and purity for each cluster and evalutes their stability, ## using the functions available in evaluate-cluster.R (on OpenscPCA portal) ## But this script is calling evaluate-cluster.R in the same directory (not sure how to call from OpenscPCA portal) library(Seurat) library(dplyr) -run_eval <- function(ind.lib){ - seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) - clusID.df <- data.frame(FetchData(seu, vars = "leiden_clusters"))|> tibble::rownames_to_column(var = "cell_id") +run_eval <- function(ind.lib) { + seu <- readRDS(file.path(out_loc, "results/rds", paste0(ind.lib, ".rds"))) + clusID.df <- data.frame(FetchData(seu, vars = "leiden_clusters")) |> tibble::rownames_to_column(var = "cell_id") colnames(clusID.df)[2] <- "cluster" cluster_df1 <- rOpenScPCA::calculate_silhouette(x = seu, cluster_df = clusID.df, pc_name = "Xpca_") cluster_df2 <- rOpenScPCA::calculate_purity(x = seu, cluster_df = clusID.df, pc_name = "Xpca_") - final_df <- merge(cluster_df1, cluster_df2, by = c("cell_id","cluster")) - perClus_df <- final_df %>% group_by(cluster) %>% + final_df <- merge(cluster_df1, cluster_df2, by = c("cell_id", "cluster")) + perClus_df <- final_df %>% + group_by(cluster) %>% summarise(avgSil = mean(silhouette_width), avgPur = mean(purity)) %>% data.frame() - stability_df <- rOpenScPCA::calculate_stability(x = seu, clusters = clusID.df$cluster, - pc_name = "Xpca_",algorithm = "leiden", - resolution = 1.0, objective_function = "modularity", - seed = 10) - write.table(final_df, sep = "\t", row.names = F, quote = F, - file = file.path(out_loc,"results/evalClus/",paste0(ind.lib,"_sil-purity_perCell.txt"))) - write.table(stability_df, sep = "\t", row.names = F, quote = F, - file = file.path(out_loc,"results/evalClus/",paste0(ind.lib,"_stability.txt"))) - write.table(perClus_df, sep = "\t", row.names = F, quote = F, - file = file.path(out_loc,"results/evalClus/",paste0(ind.lib,"_avgSil-purity_perClus.txt"))) + stability_df <- rOpenScPCA::calculate_stability( + x = seu, cluster_df = clusID.df, + pc_name = "Xpca_", algorithm = "leiden", + resolution = 1.0, objective_function = "modularity", + seed = 10 + ) + write.table(final_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_sil-purity_perCell.txt")) + ) + write.table(stability_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_stability.txt")) + ) + write.table(perClus_df, + sep = "\t", row.names = F, quote = F, + file = file.path(out_loc, "results/evalClus/", paste0(ind.lib, "_avgSil-purity_perClus.txt")) + ) } -project_root <- rprojroot::find_root(rprojroot::is_git_root) +project_root <- rprojroot::find_root(rprojroot::is_git_root) projectID <- "SCPCP000003" out_loc <- file.path(project_root, "analyses/cell-type-nonETP-ALL-03") -data_loc <- file.path(project_root, "data/current",projectID) +data_loc <- file.path(project_root, "data/current", projectID) dir.create(file.path(out_loc, "results/evalClus"), showWarnings = FALSE) -#loading functions for evaluating clusters -#source(file.path(out_loc,"scripts/evaluate-clusters.R")) +# loading functions for evaluating clusters +# source(file.path(out_loc,"scripts/evaluate-clusters.R")) -metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- read.table(file.path(data_loc, "single_cell_metadata.tsv"), sep = "\t", header = T) metadata <- metadata[which(metadata$scpca_project_id == projectID & - metadata$diagnosis == "Non-early T-cell precursor T-cell acute lymphoblastic leukemia"), ] + metadata$diagnosis == "Non-early T-cell precursor T-cell acute lymphoblastic leukemia"), ] libraryID <- metadata$scpca_library_id purrr::walk(libraryID, run_eval) - diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/sctype_exploration.R b/analyses/cell-type-nonETP-ALL-03/scripts/06_sctype_exploration.R similarity index 100% rename from analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/sctype_exploration.R rename to analyses/cell-type-nonETP-ALL-03/scripts/06_sctype_exploration.R diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/07_run_copykat.R b/analyses/cell-type-nonETP-ALL-03/scripts/07_run_copykat.R new file mode 100644 index 000000000..8a691df9b --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/scripts/07_run_copykat.R @@ -0,0 +1,35 @@ +#!/usr/bin/env Rscript + +library(Seurat) +library(copykat) + +run_copykat <- function(ind.lib){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + annot.file <- file.path(out_loc,"results",paste0(ind.lib,"_newB-normal-annotation.txt")) + if (file.exists(annot.file)){ #the sample has new B cells annotated + annot.df <- read.table(annot.file, header=F, row.names=1, sep="\t", stringsAsFactors=FALSE, + colClasses = c('character', 'character')) + norm.cells <- rownames(annot.df)[which(annot.df$V2=="new B")] + n_cores <- parallel::detectCores() - 1 + copykat.test <- copykat(rawmat=seu@assays[["RNA"]]@counts, id.type="Ensemble", + ngene.chr=5, win.size=25, KS.cut=0.1, sam.name=ind.lib, + distance="euclidean", norm.cell.names=norm.cells, + output.seg="FALSE", plot.genes="TRUE", genome="hg20",n.cores=n_cores) + idx <- match(colnames(seu), copykat.test$prediction$cell.names) + seu$newB.copykat.pred <- copykat.test$prediction$copykat.pred[idx] + saveRDS(seu, file = file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + } +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-nonETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +setwd(file.path(out_loc,"results/copykat_output")) + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Non-early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +purrr::walk(libraryID, run_copykat) diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/00-make-gene-order-file.R b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/00-make-gene-order-file.R new file mode 100644 index 000000000..1c04ff91e --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/00-make-gene-order-file.R @@ -0,0 +1,58 @@ +#!/usr/bin/env Rscript + +project_root <- rprojroot::find_root(rprojroot::is_git_root) + +library(optparse) + +option_list <- list( + make_option( + opt_str = c("--gtf_file"), + type = "character", + default = "s3://scpca-references/homo_sapiens/ensembl-104/annotation/Homo_sapiens.GRCh38.104.gtf.gz", + help = "URI to gtf file to create gene order file." + ), + make_option( + opt_str = c("--local_ref_dir"), + type = "character", + default = file.path(project_root, "references", "infercnv_refs"), + help = "Directory to use for saving GTF file and gene order file." + ), + make_option( + opt_str = c("--scratch_dir"), + type = "character", + default = file.path(project_root, "scratch"), + help = "Path to store copied GTF file" + ) +) + +# Parse options +opt <- parse_args(OptionParser(option_list = option_list)) + +# create ref directory if doesn't already exist +fs::dir_create(opt$local_ref_dir) + +# sync gtf file to local directory +gtf_filename <- basename(opt$gtf_file) + +local_gtf_file <- file.path(opt$scratch_dir, gtf_filename) +if (!file.exists(local_gtf_file)) { + sync_call <- glue::glue("aws s3 cp {opt$gtf_file} {local_gtf_file} --no-sign-request") + system(sync_call) +} + +# define gene order file name using gtf file +gtf_basename <- stringr::str_remove(gtf_filename, ".gtf.gz") +gene_order_filename <- glue::glue("{gtf_basename}.gene_order.txt") +gene_order_file <- file.path(opt$local_ref_dir, gene_order_filename) + +# read in gtf file +gtf <- rtracklayer::import(local_gtf_file, feature.type = "gene") + +# format gene order file +gtf_df <- gtf |> + as.data.frame() |> + dplyr::select(gene_id, seqnames, start, end) |> + dplyr::mutate(seqnames = glue::glue("chr{seqnames}")) + +# export gene order file +readr::write_tsv(gtf_df, gene_order_file, col_names = FALSE) diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/copykat_exploration.R b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/copykat_exploration.R index 89ef14248..92e4ccdac 100644 --- a/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/copykat_exploration.R +++ b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/copykat_exploration.R @@ -8,31 +8,31 @@ library(dplyr) copykatInterpret <- function(annot.obj, library.id, ct.colors){ tryCatch({ - exprs <- data.frame(FetchData(annot.obj, vars = c("sctype_classification","copykat.pred","lowConfidence_annot"))) + exprs <- data.frame(FetchData(annot.obj, vars = c("sctype_classification","newB.copykat.pred","lowConfidence_annot"))) df <- exprs %>% - group_by(copykat.pred, sctype_classification) %>% + group_by(newB.copykat.pred, sctype_classification) %>% count(name = "Frequency") total_df <- df %>% - group_by(copykat.pred) %>% + group_by(newB.copykat.pred) %>% summarise(Total = sum(Frequency)) p1 <- ggplot() + - geom_bar(data = df, aes(x = copykat.pred, y = Frequency, fill = sctype_classification), width = 0.5, stat = "identity", position = "fill") + - geom_text(data = total_df, aes(y = 100, x = copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + + geom_bar(data = df, aes(x = newB.copykat.pred, y = Frequency, fill = sctype_classification), width = 0.5, stat = "identity", position = "fill") + + geom_text(data = total_df, aes(y = 100, x = newB.copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + scale_fill_manual(values = ct_color) df <- exprs %>% - dplyr::group_by(copykat.pred, lowConfidence_annot) %>% + dplyr::group_by(newB.copykat.pred, lowConfidence_annot) %>% dplyr::count(name = "Frequency") p2 <- ggplot() + - geom_bar(data = df, aes(x = copykat.pred, y = Frequency, fill = lowConfidence_annot), width = 0.5, stat = "identity", position = "fill") + - geom_text(data = total_df, aes(y = 100, x = copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + + geom_bar(data = df, aes(x = newB.copykat.pred, y = Frequency, fill = lowConfidence_annot), width = 0.5, stat = "identity", position = "fill") + + geom_text(data = total_df, aes(y = 100, x = newB.copykat.pred, label = Total), size = 4, position = position_fill(vjust = 1.02)) + scale_fill_manual(values = ct_color) cowplot::plot_grid(plotlist = list(p1,p2), nrow = 1) + cowplot::draw_figure_label(library.id, position = "top", size = 14, fontface = "bold") ggsave(file.path(out_loc,"plots/copykat_exploration",paste0(library.id,"_celltypeVScopykat.png")), width = 10, height = 5, bg = "white", dpi = 150) ### plotting blast module scores - Idents(annot.obj) <- factor(annot.obj$copykat.pred, levels = c("aneuploid","diploid","not.defined")) + Idents(annot.obj) <- factor(annot.obj$newB.copykat.pred, levels = c("aneuploid","diploid","not.defined")) VlnPlot(annot.obj, features = "Blast_Features1") + ggtitle(paste0(library.id,": Blast module score")) + NoLegend() ggsave(file.path(out_loc,"plots/copykat_exploration",paste0(library.id,"_blastModuleScore.png")), width = 6, height = 6, bg = "white", dpi = 150) }, error=function(e){}) diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/run_infercnv.R b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/run_infercnv.R new file mode 100644 index 000000000..68ab32063 --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/scripts/exploratory-analyses/run_infercnv.R @@ -0,0 +1,52 @@ +#!/usr/bin/env Rscript + +#sudo apt-get install r-cran-rjags +#This script runs inferCNV using the new B cells, and trying to identify "non-malignant" group based on the infercnv.png + +library(Seurat) + +run_inferCNV <- function(ind.lib){ + dir.create(file.path(scratch_dir, ind.lib), showWarnings = FALSE) + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + annot.file <- file.path(out_loc,"results",paste0(ind.lib,"_newB-normal-annotation.txt")) + infercnv_obj <- infercnv::CreateInfercnvObject(raw_counts_matrix=seu@assays[["RNA"]]@counts, + annotations_file=annot.file, + delim="\t", + gene_order_file=geneFile, + ref_group_names="new B") + options(scipen = 100) + infercnv_obj <- infercnv::run(infercnv_obj, + cutoff = 0.1, # use 1 for smart-seq, 0.1 for 10x-genomics + out_dir = file.path(scratch_dir,ind.lib), # save all intermediate files to scratch dir + denoise = T, HMM = T, analysis_mode = "samples", + save_rds = F, # don't save the intermediate rds files + num_threads = parallel::detectCores() - 1) + + # create table with barcodes and CNVs for each chromosome + infercnv::add_to_seurat( + seurat_obj = NULL, + infercnv_output_path = file.path(scratch_dir, ind.lib)) + + ### adding clusterID from the cutting of hierarchical clustering to seu object + final_cnv_obj <- readRDS(file.path(out_loc, "results/infercnv_output", paste0(ind.lib,"_run.final.infercnv_obj"))) + hres <- final_cnv_obj@tumor_subclusters$hc$all_observations + obs.clusID <- cutree(hres,4) #4 clusters for SCPCL000703 + hres <- dendextend::color_branches(hres, k= 4) + plot(hres) + seu$infercnv.pred <- obs.clusID[match(names(obs.clusID), colnames(seu))] +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-nonETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +scratch_dir <- file.path(out_loc,"scratch") + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Non-early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id + +geneFile <- file.path(project_root, "references/infercnv_refs/Homo_sapiens.GRCh38.104.gene_order.txt") + +purrr::walk(libraryID, run_inferCNV) diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/markerGenes_submission.R b/analyses/cell-type-nonETP-ALL-03/scripts/markerGenes_submission.R new file mode 100644 index 000000000..ceb4283b0 --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/scripts/markerGenes_submission.R @@ -0,0 +1,31 @@ +#!/usr/bin/env Rscript + +#This script converts the "Azimuth_BM_level1.csv" into the submissio format for marker genes + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-nonETP-ALL-03") + +all_sources <- c("https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow", + "doi:10.1038/s41598-023-39152-z","https://sctype.app/database.php") +gene.df <- read.table(file.path(out_loc,"Azimuth_BM_level1.csv"), sep = ",", header = T) + +df1 <- c() +for (i in 1:nrow(gene.df)){ + tmp.gene <- strsplit(gene.df$ensembl_id_positive_marker[i],",")[[1]] + if (gene.df$cellName[i] == "Blast"){ + source <- all_sources[2] + }else if (gene.df$cellName[i] %in% c("Pre Eryth","Cancer")){ + source <- all_sources[3] + }else{source <- all_sources[1]} + + df1 <- rbind(df1,data.frame(ensembl_gene_id=tmp.gene, + cell_type=rep(gene.df$cellName[i],length(tmp.gene)), + source=rep(source,length(tmp.gene)))) +} + +df2 <- by(df1, df1$ensembl_gene_id, \(x) list2DF(lapply(x, \(.) toString(unique(.))))) |> + do.call(what=rbind) + +write.table(df2, file = file.path(out_loc,"submission_markerGenes.tsv"), sep = "\t", + row.names = F, quote = F) diff --git a/analyses/cell-type-nonETP-ALL-03/scripts/writeout_submission.R b/analyses/cell-type-nonETP-ALL-03/scripts/writeout_submission.R new file mode 100644 index 000000000..dfb4a0173 --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/scripts/writeout_submission.R @@ -0,0 +1,59 @@ +#!/usr/bin/env Rscript + +library(Seurat) +library(ggplot2) + +writeout <- function(ind.lib, sample.ID, ct.colors, n.row = 1){ + seu <- readRDS(file.path(out_loc,"results/rds",paste0(ind.lib,".rds"))) + voi <- c('tumor_cell_classification','cell_type_assignment') + tumor_classification <- c('tumor','normal','unknown') + names(tumor_classification) <- c('aneuploid','diploid','not.defined') + seu$cell_type_assignment <- seu$sctype_classification + tryCatch({ + seu$tumor_cell_classification <- as.vector(tumor_classification[match(seu$newB.copykat.pred,names(tumor_classification))]) + }, error=function(e){}) + tryCatch({ + voi_df <- data.frame(FetchData(seu, vars = voi)) |> tibble::rownames_to_column(var = "cell_barcode") + }, error=function(e){}) + final.df <- data.frame(scpca_sample_id=rep(sample.ID, nrow(voi_df)), voi_df, + CL_ontology_id=gene.df$ontologyID[match(voi_df$cell_type_assignment,gene.df$cellName)]) + write.table(final.df, sep = "\t", quote = F, row.names = F, + file = file.path(out_loc,"results/submission_table",paste0(ind.lib,"_metadata.tsv"))) + + ## plotting the variables + plot.list <- list() + for (plot.type in voi){ + if (plot.type == "cell_type_assignment"){ + clrs <- ct.colors + } else{ + clrs <- NULL + } + tryCatch({ + plot.list[[plot.type]] <- DimPlot(seu, reduction = "Xumap_", group.by = plot.type, + label = T, cols = clrs, repel = T) + }, error=function(e){}) + } + cowplot::plot_grid(plotlist = plot.list, nrow = n.row) + patchwork::plot_annotation(title = ind.lib) & + theme(plot.title = element_text(hjust = 0.5, size = 18, face="bold")) + ggsave(file.path(out_loc,"results/submission_table",paste0("multipanels_",ind.lib,".png")), width = 12, height = 5, bg = "white", dpi = 150) +} + +project_root <- rprojroot::find_root(rprojroot::is_git_root) +projectID <- "SCPCP000003" +out_loc <- file.path(project_root, "analyses/cell-type-nonETP-ALL-03") +data_loc <- file.path(project_root, "data/current",projectID) +dir.create(file.path(out_loc, "results/submission_table"), showWarnings = FALSE) + +gene.df <- read.table(file.path(out_loc, "Azimuth_BM_level1.csv"), sep = ",", header = T) +ct_color <- c("darkorchid","skyblue2","dodgerblue2","gold","beige","sienna1","green4","navy", + "chocolate4","red","darkred","#6A3D9A","maroon","yellow4","grey35","black","lightpink","grey80") +names(ct_color) <- c("B","CD4 T","CD8 T","DC","HSPC","Mono","NK","Other T","Macrophage", + "Early Eryth","Late Eryth","Plasma","Platelet","Stromal","Blast","Cancer","Pre Eryth","Unknown") + +metadata <- read.table(file.path(data_loc,"single_cell_metadata.tsv"), sep = "\t", header = T) +metadata <- metadata[which(metadata$scpca_project_id == projectID & + metadata$diagnosis == "Non-early T-cell precursor T-cell acute lymphoblastic leukemia"), ] +libraryID <- metadata$scpca_library_id +sampleID <- metadata$scpca_sample_id + +purrr::walk2(libraryID, sampleID, ~ writeout(ind.lib = .x, sample.ID = .y, ct.colors = ct_color)) diff --git a/analyses/cell-type-nonETP-ALL-03/submission_markerGenes.tsv b/analyses/cell-type-nonETP-ALL-03/submission_markerGenes.tsv new file mode 100644 index 000000000..bf66d938e --- /dev/null +++ b/analyses/cell-type-nonETP-ALL-03/submission_markerGenes.tsv @@ -0,0 +1,152 @@ +ensembl_gene_id cell_type source +ENSG00000002586 Blast doi:10.1038/s41598-023-39152-z +ENSG00000005961 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000007062 Cancer https://sctype.app/database.php +ENSG00000011465 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000012124 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000014914 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000026508 Cancer https://sctype.app/database.php +ENSG00000041982 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000047457 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000048462 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000069667 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000070031 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000072274 Pre Eryth https://sctype.app/database.php +ENSG00000073754 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000075340 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000075618 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000081059 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000081237 Pre Eryth https://sctype.app/database.php +ENSG00000086205 Cancer https://sctype.app/database.php +ENSG00000088726 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000091513 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000100385 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000100450 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000101200 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000102145 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104660 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104894 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000104974 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105251 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105369 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000105610 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000106327 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000107742 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000110195 Pre Eryth https://sctype.app/database.php +ENSG00000110203 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000111057 Cancer https://sctype.app/database.php +ENSG00000111796 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112077 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112175 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000112212 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000113088 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000113140 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115232 Pre Eryth https://sctype.app/database.php +ENSG00000115461 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115607 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115718 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000115884 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000116191 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000117281 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000117632 Blast doi:10.1038/s41598-023-39152-z +ENSG00000119865 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000119888 HSPC, Late Eryth, Cancer https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow, https://sctype.app/database.php +ENSG00000121769 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000123416 Blast doi:10.1038/s41598-023-39152-z +ENSG00000124491 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000124766 Blast doi:10.1038/s41598-023-39152-z +ENSG00000125810 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000130203 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000130208 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000131016 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000132514 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000132704 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000133742 Pre Eryth https://sctype.app/database.php +ENSG00000133789 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000134545 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000135218 Pre Eryth https://sctype.app/database.php +ENSG00000135525 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000138639 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000138795 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000139193 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000139329 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000141736 Cancer https://sctype.app/database.php +ENSG00000143184 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000143297 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000144290 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000145220 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000147571 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150045 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150681 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000150687 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000152518 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000152583 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153064 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153071 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000153563 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000155367 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000156738 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000156966 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000158825 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000159189 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000160307 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000160654 CD4 T, CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000162444 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163534 B https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163554 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163687 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163736 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000163737 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000164692 Stromal https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000166211 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000166523 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000167286 CD4 T, CD8 T, Blast https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow, doi:10.1038/s41598-023-39152-z +ENSG00000167476 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000168685 CD4 T, Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000168913 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000169432 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000169704 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000170180 Pre Eryth https://sctype.app/database.php +ENSG00000170323 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000170891 HSPC, Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000171051 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172005 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172116 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172247 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000172995 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173369 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173372 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000173762 Blast doi:10.1038/s41598-023-39152-z +ENSG00000175792 Pre Eryth https://sctype.app/database.php +ENSG00000176783 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000177606 Blast doi:10.1038/s41598-023-39152-z +ENSG00000178573 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000179348 Early Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000184613 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000186074 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000186710 HSPC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000187699 Platelet https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000188672 HSPC, Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000189430 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000196188 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000197353 Mono https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198178 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198574 NK https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000198851 CD4 T, CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000204010 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211640 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211673 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000211685 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000213934 Late Eryth https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000215788 Other T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000222037 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000223609 Pre Eryth https://sctype.app/database.php +ENSG00000227191 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000227507 CD4 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000239961 DC https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000240505 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000243466 Plasma https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000244734 Pre Eryth https://sctype.app/database.php +ENSG00000250722 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000271503 CD8 T https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow +ENSG00000275385 Macrophage https://azimuth.hubmapconsortium.org/references/#Human%20-%20Bone%20Marrow diff --git a/analyses/cell-type-wilms-tumor-14/run_cell-type-wilms-14.sh b/analyses/cell-type-wilms-tumor-14/run_cell-type-wilms-14.sh index 1b634b643..d293b826d 100644 --- a/analyses/cell-type-wilms-tumor-14/run_cell-type-wilms-14.sh +++ b/analyses/cell-type-wilms-tumor-14/run_cell-type-wilms-14.sh @@ -23,6 +23,7 @@ scratch_dir="scratch" results_dir="results" data_dir="../../data/current/SCPCP000014" + meta_path="${data_dir}/single_cell_metadata.tsv" @@ -31,22 +32,26 @@ meta_path="${data_dir}/single_cell_metadata.tsv" step_name="00_preprocess_reference" scratch_dir_step="${scratch_dir}/${step_name}" && mkdir -p ${scratch_dir_step} + + # Download and process reference data ref_h5ad="${scratch_dir_step}/Fetal_full_v3.h5ad" ref_seurat="${scratch_dir_step}/kidneyatlas.rdsSeurat" ref_seurat_sct="${scratch_dir_step}/kidneyatlas_SCT.rdsSeurat" +echo "Downloading reference data" if [[ ! -e ${ref_h5ad} ]]; then ref_url="https://cellgeni.cog.sanger.ac.uk/kidneycellatlas/Fetal_full_v3.h5ad" - curl -s -o ${ref_h5ad} ${ref_url} + curl -s --show-error --ipv4 -L -o ${ref_h5ad} ${ref_url} fi +echo "Processing reference data" Rscript scripts/${step_name}.R \ --in_fetal_atlas "${ref_h5ad}" \ --out_fetal_atlas "${ref_seurat}" \ $SCT_FLAG - +echo "Preprocessing data" ## Preprocess data Rscript scripts/00_preprocessing_rds.R @@ -59,6 +64,7 @@ Rscript scripts/00_preprocessing_rds.R # --libraries SCPCL000846,SCPCL000847 \ # $TEST_FLAG +echo "running anchor transfer" # run all samples Rscript scripts/01_anchor_transfer_seurat.R \ --reference "${ref_seurat}" \ @@ -67,6 +73,7 @@ Rscript scripts/01_anchor_transfer_seurat.R \ $SCT_FLAG \ $TEST_FLAG +echo "summarizing results" Rscript scripts/summary_results.R \ --metadata "${meta_path}" \ $TEST_FLAG diff --git a/analyses/hello-clusters/README.md b/analyses/hello-clusters/README.md index 1b31ad574..8e8aab7e5 100644 --- a/analyses/hello-clusters/README.md +++ b/analyses/hello-clusters/README.md @@ -2,7 +2,7 @@ ## Description -This module provides examples of how to use clustering functionality in the `rOpenScPCA` package. +This module provides examples of how to use clustering functionality in the [`rOpenScPCA` package](https://github.com/AlexsLemonade/rOpenScPCA/). When clustering scRNA-seq data, in particular when those clusters are used in downstream analyses, it is important to evaluate the quality of the clusters. The `rOpenScPCA` package provides several functions that leverage the [`bluster` package](https://bioconductor.org/packages/release/bioc/html/bluster.html) to facilitate performing and evaluating graph-based clustering in a reproducible manner. @@ -11,6 +11,7 @@ The `rOpenScPCA` package provides several functions that leverage the [`bluster` The function `calculate_clusters()` can be used to perform graph-based clustering. By default, this function uses the Louvain algorithm with Jaccard weighting. + ### Evaluate clustering `rOpenScPCA` contains several functions to calculate quality metrics for a particular clustering result: @@ -34,14 +35,13 @@ The function `sweep_clusters()` allows you to generate clustering results from a ## Installing rOpenScPCA -The `rOpenScPCA` package is disseminated in the `OpenScPCA-analysis` repository in the `packages` directory. +The `rOpenScPCA` package is available in the [`AlexsLemonade/rOpenScPCA` repository](https://github.com/AlexsLemonade/rOpenScPCA/). + If you use this package in your analysis module, you should install and track it with `renv` as follows: ``` # First, install rOpenScPCA -renv::install("AlexsLemonade/OpenScPCA-analysis:packages/rOpenScPCA") - - +renv::install("AlexsLemonade/rOpenScPCA") # Second, run snapshot to add the package to renv.lock renv::snapshot() diff --git a/analyses/hello-clusters/renv.lock b/analyses/hello-clusters/renv.lock index 1cacfac46..06a8a8f31 100644 --- a/analyses/hello-clusters/renv.lock +++ b/analyses/hello-clusters/renv.lock @@ -1859,10 +1859,9 @@ "RemoteType": "github", "RemoteHost": "api.github.com", "RemoteUsername": "AlexsLemonade", - "RemoteRepo": "OpenScPCA-analysis", - "RemoteSubdir": "packages/rOpenScPCA", + "RemoteRepo": "rOpenScPCA", "RemoteRef": "main", - "RemoteSha": "c67fc87806fc8a497d18624d759342ef041e1030", + "RemoteSha": "f71a8191130fa543e6506c73c7b62ffa55e8ba3f", "Requirements": [ "BiocParallel", "SingleCellExperiment", @@ -1874,7 +1873,7 @@ "tibble", "tidyr" ], - "Hash": "74da2034ae461cf45a5cf115667ff4e4" + "Hash": "4b9dcd60f9ae2a2e1999acbd49d7b07a" }, "rappdirs": { "Package": "rappdirs", diff --git a/analyses/metacells/Dockerfile b/analyses/metacells/Dockerfile index 80010ba65..cda5d6f05 100644 --- a/analyses/metacells/Dockerfile +++ b/analyses/metacells/Dockerfile @@ -1,10 +1,59 @@ -# A template docker file for creating a new analysis -FROM ubuntu:22.04 +# Dockerfile for metacell analysis +FROM bioconductor/r-ver:3.19 # Labels following the Open Containers Initiative (OCI) recommendations # For more information, see https://specs.opencontainers.org/image-spec/annotations/?v=v1.0.1 LABEL org.opencontainers.image.authors="OpenScPCA scpca@ccdatalab.org" -LABEL org.opencontainers.image.source="https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/templates/analysis-module" +LABEL org.opencontainers.image.source="https://github.com/AlexsLemonade/OpenScPCA-analysis/tree/main/analyses/metacells" # Set an environment variable to allow checking if we are in an OpenScPCA container ENV OPENSCPCA_DOCKER=TRUE + +# set a name for the conda environment +ARG ENV_NAME=openscpca-metacells + +# set environment variables to install conda +ENV PATH="/opt/conda/bin:${PATH}" + +# Install conda via miniforge +# adapted from https://github.com/conda-forge/miniforge-images/blob/master/ubuntu/Dockerfile +RUN curl -L "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" -o /tmp/miniforge.sh \ + && bash /tmp/miniforge.sh -b -p /opt/conda \ + && rm -f /tmp/miniforge.sh \ + && conda clean --tarballs --index-cache --packages --yes \ + && find /opt/conda -follow -type f -name '*.a' -delete \ + && find /opt/conda -follow -type f -name '*.pyc' -delete \ + && conda clean --force-pkgs-dirs --all --yes + +# Activate conda environments in bash +RUN ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh \ + && echo ". /opt/conda/etc/profile.d/conda.sh" >> /etc/skel/.bashrc \ + && echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc + +# Install conda-lock +RUN conda install --channel=conda-forge --name=base conda-lock \ + && conda clean --all --yes + +# Install renv +RUN Rscript -e 'install.packages("renv")' \ + && rm -rf /tmp/downloaded_packages \ + && rm -rf /tmp/Rtmp* +ENV RENV_CONFIG_CACHE_ENABLED=FALSE + +# restore from conda-lock.yml file and clean up to reduce image size +COPY conda-lock.yml conda-lock.yml +RUN conda-lock install -n ${ENV_NAME} conda-lock.yml \ + && conda clean --all --yes + +# restore from renv.lock file and clean up to reduce image size +COPY renv.lock renv.lock +RUN Rscript -e 'renv::restore()' \ + && rm -rf ~/.cache/R/renv \ + && rm -rf /tmp/downloaded_packages \ + && rm -rf /tmp/Rtmp* + +# Activate conda environment on bash launch +RUN echo "conda activate ${ENV_NAME}" >> ~/.bashrc + +# Set CMD to bash to activate the environment when launching +CMD ["/bin/bash"] diff --git a/analyses/metacells/conda-lock.yml b/analyses/metacells/conda-lock.yml index 180cb4b28..bf1f8a404 100644 --- a/analyses/metacells/conda-lock.yml +++ b/analyses/metacells/conda-lock.yml @@ -13,9 +13,9 @@ version: 1 metadata: content_hash: - linux-64: 5067a29fa9271c8d282313d0c132e7595558982e79fbfb790c04e90238f9097e - osx-64: 44628b6e05a20ecb1997397a1d21e1bee74df8fb30536c0650783a7e35490a1f - osx-arm64: 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category: main optional: false - name: anndata - version: 0.10.8 + version: 0.11.0 manager: conda platform: osx-64 dependencies: @@ -134,14 +134,14 @@ package: h5py: '>=3.1' array-api-compat: '>1.4,!=1.5' pandas: '>=1.4,!=2.1.2' - url: https://conda.anaconda.org/conda-forge/noarch/anndata-0.10.8-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/anndata-0.11.0-pyhd8ed1ab_0.conda hash: - md5: 69332bd10887c9a18c15bc9e28ad8f58 - sha256: 5035a9d56c1bf104b32e459b6028aa93e24d8433ec79b7fec1021241b58fb26e + md5: f3e835932ea8dc8db171495edbf0b8c4 + sha256: dc7af877eb313e19f810051cb1f799d25c5dc66faac35452314491f2826a2041 category: main optional: false - name: anndata - version: 0.10.8 + version: 0.11.0 manager: conda platform: osx-arm64 dependencies: @@ -154,10 +154,10 @@ package: h5py: '>=3.1' array-api-compat: '>1.4,!=1.5' pandas: '>=1.4,!=2.1.2' - url: https://conda.anaconda.org/conda-forge/noarch/anndata-0.10.8-pyhd8ed1ab_0.conda + url: 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https://conda.anaconda.org/conda-forge/noarch/anyio-4.4.0-pyhd8ed1ab_0.conda + url: https://conda.anaconda.org/conda-forge/noarch/anyio-4.6.2.post1-pyhd8ed1ab_0.conda hash: - md5: 1fa97c6e8db1f82c64ff17a5efc4ae8e - sha256: 84ac9429812495f12939ab4994f2634f7cacd254f6234a0c2c0243daed15a7ee + md5: 688697ec5e9588bdded167d19577625b + sha256: 4b54b7ce79d818e3cce54ae4d552dba51b7afac160ceecdefd04b3917a37c502 category: main optional: false - name: appdirs @@ -354,14 +354,15 @@ package: manager: conda platform: linux-64 dependencies: + __glibc: '>=2.17,<3.0.a0' cffi: '>=1.0.1' - libgcc-ng: '>=12' + libgcc: '>=13' python: '>=3.11,<3.12.0a0' python_abi: 3.11.* - url: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py311h459d7ec_4.conda + url: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-21.2.0-py311h9ecbd09_5.conda hash: - md5: de5b16869a430949b02161b04b844a30 - sha256: 104194af519b4e667aa5341068b94b521a791aaaa05ec0091f8f0bdba43a60ac + md5: 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"https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" -o /tmp/miniforge.sh \ + && bash /tmp/miniforge.sh -b -p /opt/conda \ + && rm -f /tmp/miniforge.sh \ + && conda clean --tarballs --index-cache --packages --yes \ + && find /opt/conda -follow -type f -name '*.a' -delete \ + && find /opt/conda -follow -type f -name '*.pyc' -delete \ + && conda clean --force-pkgs-dirs --all --yes + +# Activate conda environments in bash +RUN ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh \ + && echo ". /opt/conda/etc/profile.d/conda.sh" >> /etc/skel/.bashrc \ + && echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc + +# Install conda-lock +RUN conda install --channel=conda-forge --name=base conda-lock \ + && conda clean --all --yes + +# Copy conda lock file to image +COPY conda-lock.yml conda-lock.yml + +# restore from conda-lock.yml file and clean up to reduce image size +RUN conda-lock install -n ${ENV_NAME} conda-lock.yml \ + && conda clean --all --yes + +# Activate conda environment on bash launch +RUN echo "conda activate ${ENV_NAME}" >> ~/.bashrc + ENV RENV_CONFIG_CACHE_ENABLED=FALSE RUN Rscript -e "install.packages(c('remotes', 'renv'))" @@ -27,5 +59,7 @@ ENV BASILISK_EXTERNAL_DIR=/usr/local/renv/basilisk RUN Rscript -e "proc <- basilisk::basiliskStart(env = zellkonverter::zellkonverterAnnDataEnv(), testload = 'anndata'); \ basilisk::basiliskStop(proc)" + + # set final workdir for commands WORKDIR /home diff --git a/analyses/simulate-sce/README.md b/analyses/simulate-sce/README.md index 1b2814dce..8a84e4941 100644 --- a/analyses/simulate-sce/README.md +++ b/analyses/simulate-sce/README.md @@ -11,6 +11,14 @@ Before running the scripts here, open R in this directory and run the following renv::restore() ``` +The AnnData conversion requires the `anndata` and `pandas` packages, which are included in the `openscpca-simulate-sce` conda environment. +To install and activate this environment, use the following commands: + +```bash +conda-lock install -n openscpca-simulate-sce +conda activate openscpca-simulate-sce +``` + ### Data download Data from projects to be processed should be downloaded from the OpenScPCA release bucket using the `download-data.py` script in the repository root directory. diff --git a/analyses/simulate-sce/conda-lock.yml b/analyses/simulate-sce/conda-lock.yml new file mode 100644 index 000000000..d1f3b3096 --- /dev/null +++ b/analyses/simulate-sce/conda-lock.yml @@ -0,0 +1,5845 @@ +# This lock file was generated by conda-lock (https://github.com/conda/conda-lock). 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+ - pandas diff --git a/analyses/simulate-sce/scripts/move-anndata-counts.R b/analyses/simulate-sce/scripts/move-anndata-counts.R deleted file mode 100644 index 1c6ff14e8..000000000 --- a/analyses/simulate-sce/scripts/move-anndata-counts.R +++ /dev/null @@ -1,48 +0,0 @@ -#!/usr/bin/env Rscript - -# Script to move the logcounts layer to the X slot in an anndata object - -library(basilisk) -library(optparse) - -# Parse arguments -------------------------------------------------------------- -# set up arguments -option_list <- list( - make_option( - opt_str = c("-d", "--dir"), - type = "character", - help = "Path where AnnData files are located. Will search recursively for files ending in `_processed_*.h5ad", - ) -) - -opts <- parse_args(OptionParser(option_list = option_list)) - -# get processed files - -anndata_files <- list.files( - opts$dir, - pattern = "_processed_.+\\.h5ad$", - recursive = TRUE, - full.names = TRUE -) - - -# load basilisk -proc <- basiliskStart(env = zellkonverter::zellkonverterAnnDataEnv(), testload = "anndata") -on.exit(basiliskStop(proc)) - - -# run a function in the basilisk environment to move elements of files -basiliskRun(proc, fun = function(files) { - adata <- reticulate::import("anndata") - for (afile in files) { - h5ad <- adata$read_h5ad(afile) - if (!is.null(h5ad$layers$get("logcounts"))) { - h5ad$raw <- h5ad - h5ad$X <- h5ad$layers["logcounts"] - h5ad$uns$X_name <- "logcounts" - h5ad$layers["logcounts"] <- NULL - h5ad$write_h5ad(afile, compression = "gzip") - } - } -}, files = anndata_files) diff --git a/analyses/simulate-sce/scripts/permute-metadata.R b/analyses/simulate-sce/scripts/permute-metadata.R index bd5c15619..24e5244f9 100755 --- a/analyses/simulate-sce/scripts/permute-metadata.R +++ b/analyses/simulate-sce/scripts/permute-metadata.R @@ -46,9 +46,14 @@ library_fields <- c( "seq_unit", "technology", "filtered_cell_count", + "filtered_spots", + "unfiltered_spots", + "tissue_spots", "submitter", "pi_name", - "project_title" + "project_title", + "demux_samples", + "demux_cell_count_estimate" ) # fields that apply at sample level @@ -81,7 +86,9 @@ processing_fields <- c( "genome_assembly", "has_cellhash", "includes_anndata", + "is_cell_line", "is_multiplexed", + "is_xenograft", "has_citeseq", "adt_filtering_method", "adt_normalization_method", @@ -92,23 +99,27 @@ processing_fields <- c( "prob_compromised_cutoff", "processed_cells", "salmon_version", + "spaceranger_version", "total_reads", "transcript_type", "unfiltered_cells", + "demux_method", "workflow", "workflow_commit", "workflow_version" ) -# Remove project-specific columns -match_cols <- sort(match(c(library_fields, sample_fields, processing_fields), colnames(metadata))) -metadata <- metadata[, match_cols] +# get project-specific columns, which should also be sample-specific +project_fields <- setdiff(colnames(metadata), c(library_fields, sample_fields, processing_fields)) # get sample metadata only & reduce to one line per sample -sample_metadata <- metadata[, sample_fields] |> dplyr::distinct() +sample_metadata <- metadata[, c(sample_fields, project_fields)] |> dplyr::distinct() # check that sample data are not repeated -stopifnot(length(unique(sample_metadata$scpca_sample_id)) == nrow(sample_metadata)) +stopifnot( + "Sample data seem to be repeated, metadata permutation failed" = + length(unique(sample_metadata$scpca_sample_id)) == nrow(sample_metadata) +) # permute sample metadata ------------------------------------------------------------- diagnosis_order <- sample(seq(1, nrow(sample_metadata)), nrow(sample_metadata)) @@ -130,6 +141,11 @@ sample_metadata <- sample_metadata |> submitter_id = "" # remove submitter_id, ) +# permute project-specific columns +for (f in project_fields) { + sample_metadata[[f]] <- sample(sample_metadata[[f]]) +} + metadata <- metadata |> dplyr::rows_update(sample_metadata, by = "scpca_sample_id") diff --git a/analyses/simulate-sce/scripts/reformat_anndata.py b/analyses/simulate-sce/scripts/reformat_anndata.py new file mode 100755 index 000000000..ff2d0f2fb --- /dev/null +++ b/analyses/simulate-sce/scripts/reformat_anndata.py @@ -0,0 +1,79 @@ +#!/usr/bin/env python3 +""" +This script takes an AnnData object and checks for the `logcounts` in layers. +If present, `logcounts` is moved to `X` and `X` (which has the raw counts) is moved to `raw.X` + +In addition, any DataFrames in `obsm` are converted to ndarrays, highly variable genes are converted to a `var` column. +If a pca metadata file is found, PCA variance statistics and standard creation values are in the format expected by scanpy. + +Adapted from https://github.com/AlexsLemonade/scpca-nf/blob/v0.8.5/bin/reformat_anndata.py +Changed to work on a directory of files instead of a single file; only operates on the processed data files +""" + +import argparse +import pathlib + +import anndata +import pandas as pd + + +def reformat_anndata(anndata_file, pca_metafile): + # read in anndata + adata = anndata.read_h5ad(anndata_file) + + # if logcounts is present + if "logcounts" in adata.layers: + # move X to raw.X by creating the raw adata + adata.raw = adata + # move logcounts to X and rename + adata.X = adata.layers["logcounts"] + adata.uns["X_name"] = "logcounts" + del adata.layers["logcounts"] + + # convert DataFrames in obsm to ndarrays + for key, value in adata.obsm.items(): + if isinstance(value, pd.DataFrame): + adata.obsm[key] = value.to_numpy() + + # add pca adata to uns if pca_meta_file is provided in the format created by scanpy + if pathlib.Path(pca_metafile).exists(): + pca_meta = pd.read_csv(pca_metafile, sep="\t", index_col=0) + if ( + "variance" not in pca_meta.columns + or "variance_ratio" not in pca_meta.columns + ): + raise ValueError( + "`pca_meta_file` must contain columns `variance` and `variance_ratio`" + ) + pca_object = { + "param": { + "zero_center": True, + "use_highly_variable": False, + "mask_var": None, + }, + "variance": pca_meta["variance"].to_numpy(), + "variance_ratio": pca_meta["variance_ratio"].to_numpy(), + } + adata.uns["pca"] = pca_object + + # export adata + adata.write_h5ad(anndata_file, compression="gzip") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument( + "-d", + "--dir", + help="directory containing H5AD files and PCA metadata", + required=True, + ) + + args = parser.parse_args() + + # find all processed rna h5ad files in the directory, recursively + anndata_files = list(pathlib.Path(args.dir).rglob("*_processed_rna.h5ad")) + pca_files = [str(f).replace("_rna.h5ad", "_pca.tsv") for f in anndata_files] + + for anndata_file, pca_file in zip(anndata_files, pca_files): + reformat_anndata(anndata_file, pca_file) diff --git a/analyses/simulate-sce/scripts/sce-to-anndata.R b/analyses/simulate-sce/scripts/sce-to-anndata.R index bf060a405..602045392 100755 --- a/analyses/simulate-sce/scripts/sce-to-anndata.R +++ b/analyses/simulate-sce/scripts/sce-to-anndata.R @@ -6,7 +6,8 @@ # The AnnData objects being exported by this script is formatted to fit CZI schema: 3.0.0 -# adapted from https://github.com/AlexsLemonade/scpca-nf/blob/8bef82d853d19e5aeddd75401aa54cf8bfbced13/bin/sce_to_anndata.R +# adapted from https://github.com/AlexsLemonade/scpca-nf/blob/v0.8.5/bin/sce_to_anndata.R +# changed to work on a directory of files instead of a single file library(optparse) @@ -43,13 +44,19 @@ outfile_name <- function(sce_filename, feature) { return(feature_filename) } +pca_metafile_name <- function(sce_filename) { + # create the pca table file name for each feature + pca_filename <- stringr::str_replace(sce_filename, ".rds$", "_pca.tsv") + return(pca_filename) +} + # Merged object function ------------------------------------------------------ format_merged_sce <- function(sce) { # this function updates merged object formatting for anndata export # paste X to any present reduced dim names - reducedDimNames(sce) <- glue::glue("X_{reducedDimNames(sce)}") + reducedDimNames(sce) <- glue::glue("X_{tolower(reducedDimNames(sce))}") return(sce) } @@ -105,7 +112,7 @@ format_czi <- function(sce) { rowData(sce)$feature_is_filtered <- FALSE # paste X to any present reduced dim names - reducedDimNames(sce) <- glue::glue("X_{reducedDimNames(sce)}") + reducedDimNames(sce) <- glue::glue("X_{tolower(reducedDimNames(sce))}") return(sce) } @@ -115,10 +122,6 @@ convert_sce_file <- function(sce_file) { # read in sce sce <- readr::read_rds(sce_file) - # grab sample metadata - # we need this if we have any feature data that we need to add it o - sample_metadata <- metadata(sce)$sample_metadata - # make main sce czi compliant for single objects, or format merged objects sce <- format_czi(sce) @@ -131,6 +134,18 @@ convert_sce_file <- function(sce_file) { compression = "gzip" ) |> suppressMessages() # suppress notes about metadata conversion + # Get PCA metadata for AnnData + if ("X_pca" %in% reducedDimNames(sce)) { + pca_meta_df <- data.frame( + PC = 1:ncol(reducedDims(sce)$X_pca), + variance = attr(reducedDims(sce)$X_pca, "varExplained"), + variance_ratio = attr(reducedDims(sce)$X_pca, "percentVar") / 100 + ) + + # write pca to tsv + readr::write_tsv(pca_meta_df, pca_metafile_name(sce_file)) + } + # convert altExps to AnnData altExpNames(sce) |> purrr::walk(\(feature_name){ diff --git a/analyses/simulate-sce/simulate-project.sh b/analyses/simulate-sce/simulate-project.sh index d27f09af6..d8dcd7a78 100644 --- a/analyses/simulate-sce/simulate-project.sh +++ b/analyses/simulate-sce/simulate-project.sh @@ -38,4 +38,4 @@ done echo "Creating AnnData files" Rscript scripts/sce-to-anndata.R --dir $output_dir/$project -Rscript scripts/move-anndata-counts.R --dir $output_dir/$project +python scripts/reformat_anndata.py --dir $output_dir/$project diff --git a/analyses/simulate-sce/simulation-metadata.md b/analyses/simulate-sce/simulation-metadata.md index dfe3b5cf1..1709e4f16 100644 --- a/analyses/simulate-sce/simulation-metadata.md +++ b/analyses/simulate-sce/simulation-metadata.md @@ -5,7 +5,7 @@ Most contents are preserved in original form, but some are recalculated from the ## Permutation of `single_cell_metadata.tsv` -Fields that are not present in all projects (i.e. submitter-specific fields) are removed from the permuted metadata file. +Fields that are not present in all projects (i.e. submitter-specific fields) are permuted. | Field | Contents | Simulation plan | | ------------------------------------------ | -------- | -------------------------------- | @@ -15,10 +15,14 @@ Fields that are not present in all projects (i.e. submitter-specific fields) are | `seq_unit` | | keep | | `technology` | | keep | | `filtered_cell_count` | | keep | +| `filtered_spots` | | keep | +| `unfiltered_spots` | | keep | +| `tissue_spots` | | keep | | `submitter_id` | | removed | | `participant_id` | | anonymized | | `submitter` | | keep | -| `age_at_diagnosis` | | permuted | +| `age` | | permuted | +| `age_timing` | | permuted | | `sex` | | permuted | | `diagnosis` | | permuted | | `subdiagnosis` | | permuted (match diagnosis) | @@ -41,7 +45,9 @@ Fields that are not present in all projects (i.e. submitter-specific fields) are | `genome_assembly` | | keep | | `has_cellhash` | | keep | | `includes_anndata` | | keep | +| `is_cell_line` | | keep | | `is_multiplexed` | | keep | +| `is_xenograft` | | keep | | `has_citeseq` | | keep | | `adt_filtering_method` | | keep | | `adt_normalization_method` | | keep | @@ -52,6 +58,7 @@ Fields that are not present in all projects (i.e. submitter-specific fields) are | `prob_compromised_cutoff` | | keep | | `processed_cells` | | keep | | `salmon_version` | | keep | +| `spaceranger_version` | | keep | | `total_reads` | | keep | | `transcript_type` | | keep | | `unfiltered_cells` | | keep | @@ -59,6 +66,7 @@ Fields that are not present in all projects (i.e. submitter-specific fields) are | `workflow_commit` | | keep | | `workflow_version` | | keep | + ## Data matrices | Field | Contents | Simulation plan | diff --git a/packages/README.md b/packages/README.md deleted file mode 100644 index 3962960b2..000000000 --- a/packages/README.md +++ /dev/null @@ -1,9 +0,0 @@ -This directory contains packages written for use with [OpenScPCA analysis modules](https://openscpca.readthedocs.io/en/latest/contributing-to-analyses/analysis-modules/). - -The recommended version for use is given in the table below. -Please see the individual package `README` files for installation instructions. - -| Package | Description | Version | -| ------------ | ---------------------------------------- | ------- | -| `rOpenScPCA` | R functions for use in OpenScPCA modules | `0.1.0` | - diff --git a/packages/rOpenScPCA/.Rbuildignore b/packages/rOpenScPCA/.Rbuildignore deleted file mode 100644 index d82130280..000000000 --- a/packages/rOpenScPCA/.Rbuildignore +++ /dev/null @@ -1,4 +0,0 @@ -^renv$ -^renv\.lock$ -^.*\.Rproj$ -^\.Rproj\.user$ diff --git a/packages/rOpenScPCA/DESCRIPTION b/packages/rOpenScPCA/DESCRIPTION deleted file mode 100644 index 30cb1475a..000000000 --- a/packages/rOpenScPCA/DESCRIPTION +++ /dev/null @@ -1,42 +0,0 @@ -Package: rOpenScPCA -Type: Package -Title: Utility Functions for OpenScPCA Project Analysis Modules -Version: 0.1.0 -Authors@R: c( - person(c("Stephanie", "J."), "Spielman", - email = "stephanie.spielman@ccdatalab.org", - comment = list(ORCID = "0000-0002-9090-4788"), - role = c("aut", "cre")), - person(c("Joshua", "A."), "Shapiro", - email = "josh.shapiro@ccdatalab.org", - comment = list(ORCID = "0000-0002-6224-0347"), - role = c("aut")) - ) -Maintainer: Stephanie J. Spielman -Description: This package contains utility functions that support single-cell RNA-seq - analysis in R-based OpenScPCA analysis module code. -License: BSD_3_clause + file LICENSE -Encoding: UTF-8 -LazyData: true -Suggests: - testthat (>= 3.0.0), - scater, - Seurat, - splatter -Config/testthat/edition: 3 -RoxygenNote: 7.3.2 -Imports: - BiocParallel, - bluster (>= 1.14), - dplyr, - methods, - pdfCluster, - purrr, - SingleCellExperiment, - tibble, - tidyr -biocViews: - GeneExpression, - Transcriptomics, - SingleCell, - Clustering diff --git a/packages/rOpenScPCA/LICENSE b/packages/rOpenScPCA/LICENSE deleted file mode 100644 index fd378cd44..000000000 --- a/packages/rOpenScPCA/LICENSE +++ /dev/null @@ -1,195 +0,0 @@ -Copyright (c) 2024 OpenScPCA Project Maintainers & Contributors - -* All content is available for re-use under the CC-BY 4.0 license ([see section](#creative-commons-attribution-40-international) below). - -* Code blocks contained within any computational notebooks or source code files (e.g., `*.R`, `*.sh` or `*.py`) are also available for re-use under the BSD 3-Clause License ([see section](#bsd-3-clause-license) below). - -# Creative Commons Attribution 4.0 International - -Creative Commons Corporation (“Creative Commons”) is not a law firm and does not provide legal services or legal advice. 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For the avoidance of doubt, this paragraph does not form part of the public licenses. - -Creative Commons may be contacted at creativecommons.org -``` - -# BSD 3-Clause License - -_Copyright (c) 2024, OpenScPCA Project Maintainers & Contributors_ -_All rights reserved._ - -Redistribution and use in source and binary forms, with or without -modification, are permitted provided that the following conditions are met: - -* Redistributions of source code must retain the above copyright notice, this - list of conditions and the following disclaimer. - -* Redistributions in binary form must reproduce the above copyright notice, - this list of conditions and the following disclaimer in the documentation - and/or other materials provided with the distribution. - -* Neither the name of the copyright holder nor the names of its - contributors may be used to endorse or promote products derived from - this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" -AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE -IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE -DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE -FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL -DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR -SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, -OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE -OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/packages/rOpenScPCA/NAMESPACE b/packages/rOpenScPCA/NAMESPACE deleted file mode 100644 index 780875267..000000000 --- a/packages/rOpenScPCA/NAMESPACE +++ /dev/null @@ -1,10 +0,0 @@ -# Generated by roxygen2: do not edit by hand - -export(calculate_clusters) -export(calculate_purity) -export(calculate_silhouette) -export(calculate_stability) -export(extract_pc_matrix) -export(sweep_clusters) -import(SingleCellExperiment) -import(methods) diff --git a/packages/rOpenScPCA/R/calculate-clusters.R b/packages/rOpenScPCA/R/calculate-clusters.R deleted file mode 100644 index 1b50b0e23..000000000 --- a/packages/rOpenScPCA/R/calculate-clusters.R +++ /dev/null @@ -1,249 +0,0 @@ -#' Calculate graph-based clusters from a provided matrix -#' -#' This function is provided to simplify application of bluster package clustering functions on OpenScPCA data. -#' In particular, this function runs bluster::clusterRows() with the bluster::NNGraphParam() function on a -#' principal components matrix, provided either directly or via single-cell object. -#' Note that defaults for some arguments may differ from the bluster::NNGraphParam() defaults. -#' Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme to "jaccard" -#' to align with common practice in scRNA-seq analysis. -#' -#' @import methods -#' -#' @param x An object containing PCs that clustering can be performed in. This can be either a SingleCellExperiment -#' object, a Seurat object, or a matrix where columns are PCs and rows are cells. If a matrix is provided, it must -#' have row names of cell ids (e.g., barcodes). -#' @param algorithm Clustering algorithm to use. Must be one of "louvain" (default), "walktrap", or "leiden". -#' @param weighting Weighting scheme to use. Must be one of "jaccard" (default), "rank", or "number" -#' @param nn Number of nearest neighbors. Default is 10. -#' @param resolution Resolution parameter used by louvain and leiden clustering only. Default is 1. -#' @param objective_function Leiden-specific parameter for whether to use the Constant Potts Model ("CPM"; default) or "modularity" -#' @param cluster_args List of additional arguments to pass to the chosen clustering function. -#' Only single values for each argument are supported (no vectors or lists). -#' See igraph documentation for details on each clustering function: https://igraph.org/r/html/latest -#' @param threads Number of threads to use. Default is 1. -#' @param seed Random seed to set for clustering. -#' @param pc_name Name of principal components slot in provided object. This argument is only used if a SingleCellExperiment -#' or Seurat object is provided. If not provided, the SingleCellExperiment object name will default to "PCA" and the -#' Seurat object name will default to "pca". -#' -#' @return A data frame of cluster results with columns `cell_id` and `cluster`. Additional columns represent algorithm parameters -#' and include at least: `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also include `resolution`, and -#' leiden clustering will further include `objective_function`. -#' -#' @export -#' -#' @examples -#' \dontrun{ -#' # cluster PCs from a SingleCellExperiment object using default parameters and -#' # a random seed for reproducibility -#' cluster_df <- calculate_clusters(sce_object, seed = 11) -#' -#' # cluster PCs from a SingleCellExperiment object using default parameters and 4 threads -#' cluster_df <- calculate_clusters(sce_object, threads = 4, seed = 11) -#' -#' # cluster PCs from a Seurat object using default parameters -#' cluster_df <- calculate_clusters(seurat_object, seed = 11) -#' -#' # cluster directly from a matrix using default parameters -#' cluster_df <- calculate_clusters(pca_matrix, seed = 11) -#' -#' # cluster directly from a matrix using the leiden algorithm with a resolution of 0.1 -#' cluster_df <- calculate_clusters( -#' pca_matrix, -#' algorithm = "leiden", -#' resolution = 0.1, -#' seed = 11 -#' ) -#' -#' # cluster directly from a matrix using the leiden algorithm with 3 iterations -#' cluster_df <- calculate_clusters( -#' pca_matrix, -#' algorithm = "leiden", -#' cluster_args = list(n_iterations = 3), -#' seed = 11 -#' ) -#' } -calculate_clusters <- function( - x, - algorithm = c("louvain", "walktrap", "leiden"), - weighting = c("jaccard", "rank", "number"), - nn = 10, - resolution = 1, # louvain or leiden - objective_function = c("CPM", "modularity"), # leiden only - cluster_args = list(), - threads = 1, - seed = NULL, - pc_name = NULL) { - if (!is.null(seed)) { - set.seed(seed) - } - - # check and prepare matrix - pca_matrix <- prepare_pc_matrix(x, pc_name = pc_name) - - # Check input arguments - stopifnot( - "`resolution` must be numeric" = is.numeric(resolution), - "`nn` must be numeric" = is.numeric(nn), - "`threads` must be numeric" = is.numeric(threads) - ) - - algorithm <- match.arg(algorithm) - weighting <- match.arg(weighting) - objective_function <- match.arg(objective_function) - - if (length(cluster_args)) { - stopifnot( - "`cluster_args` must be a named list." = is.list(cluster_args) && !("" %in% allNames(cluster_args)), - "`cluster_args` elements must all have only a single value" = all(sapply(cluster_args, length) == 1) - ) - } - - # Update cluster_args list with parameters that users can directly provide - # note that clusterRows throws an error if this list has a param not used by the chosen algorithm - if (algorithm %in% c("louvain", "leiden")) { - cluster_args$resolution <- resolution - } - if (algorithm == "leiden") { - cluster_args$objective_function <- objective_function - } - - if (threads > 1) { - bp_param <- BiocParallel::MulticoreParam(threads) - } else { - bp_param <- BiocParallel::SerialParam() - } - - - # Perform clustering - clusters <- bluster::clusterRows( - pca_matrix, - bluster::NNGraphParam( - k = nn, - type = weighting, - cluster.fun = algorithm, - cluster.args = cluster_args, - BPPARAM = bp_param - ) - ) - - # Transform results into a table and return - cluster_df <- data.frame( - cell_id = rownames(pca_matrix), - cluster = clusters, - algorithm = algorithm, - weighting = weighting, - nn = nn - ) - - # Add in cluster_args if it has parameters to include - if (length(cluster_args) != 0) { - cluster_df <- cluster_df |> - dplyr::bind_cols( - data.frame(cluster_args) - ) - } - - return(cluster_df) -} - - - -#' Extract a principal components (PC) matrix from either a SingleCellExperiment -#' or a Seurat object. -#' -#' This function first determines if the provided object is a SingleCellExperiment or -#' Seurat object, and then extract the PC matrix. If no name for the PC matrix is provided, -#' this function will assume the name of "PCA" for SingleCellExperiment objects, and -#' "pca" for Seurat objects. -#' -#' @import SingleCellExperiment -#' @import methods -#' -#' @param sc_object Either a SingleCellExperiment or Seurat object -#' @param pc_name Optionally, the name of the PC matrix in the object. If this is -#' not provided, the name "PCA" is assumed for SingleCellExperiment objects, and -#' "pca" for Seurat objects. -#' -#' @return PC matrix with row names -#' -#' @export -#' -#' @examples -#' \dontrun{ -#' # extract PC matrix from SCE object, assuming default name "PCA" -#' pca_matrix <- extract_pc_matrix(sce_object) -#' -#' # extract PC matrix from SCE object with non-default name "PCA_MAT" -#' pca_matrix <- extract_pc_matrix(sce_object, pc_name = "PCA_MAT") -#' -#' # extract PC matrix from Seurat object, assuming default name "pca" -#' pca_matrix <- extract_pc_matrix(seurat_object) -#' } -extract_pc_matrix <- function(sc_object, pc_name = NULL) { - # default PC names for each type of object to use if - # pc_name is NULL - default_sce <- "PCA" - default_seurat <- "pca" - - if (is(sc_object, "SingleCellExperiment")) { - pc_name <- ifelse(is.null(pc_name), default_sce, pc_name) - stopifnot( - "Could not find a PC matrix in the SingleCellExperiment object." = - pc_name %in% reducedDimNames(sc_object) - ) - - pca_matrix <- reducedDim(sc_object, pc_name) - } else if (is(sc_object, "Seurat")) { - pc_name <- ifelse(is.null(pc_name), default_seurat, pc_name) - stopifnot( - "Seurat package must be installed to process a Seurat object" = - requireNamespace("Seurat", quietly = TRUE), - "Could not find a PC matrix in the Seurat object." = - pc_name %in% names(sc_object@reductions) - ) - - pca_matrix <- Seurat::Embeddings( - sc_object, - reduction = pc_name - ) - } else { - stop("You must provide a SingleCellExperiment or Seurat object.") - } - - # Ensure row names are present - stopifnot( - "The extracted PCA matrix does not have row names." = is.character(rownames(pca_matrix)) - ) - - return(pca_matrix) -} - - - - - - -#' Helper function to check and/or extract a matrix of PCs from a given object -#' -#' @param x Either a matrix of principal components (PCs), or a SingleCellExperiment -#' or Seurat object containing PCs. If a matrix is provided, rows should be cells -#' and columns should be PCs, and row names should be cell ids (e.g., barcodes). -#' @param pc_name Optionally, the name of the PC matrix in the object. Not used for -#' matrices. If this is not provided, the name "PCA" is assumed for -#' SingleCellExperiment objects, and "pca" for Seurat objects. -#' -#' @return A matrix of PCs with row names representing cell ids -prepare_pc_matrix <- function(x, pc_name = NULL) { - if (any(class(x) %in% c("matrix", "Matrix"))) { - stopifnot( - "The matrix must have row names representing cell ids, e.g. barcodes." = is.character(rownames(x)) - ) - } else if (is(x, "SingleCellExperiment") || is(x, "Seurat")) { - x <- extract_pc_matrix(x, pc_name = pc_name) - } else { - stop("The first argument should be one of: a SingleCellExperiment object, a Seurat object, or a matrix with row names.") - } - - return(x) -} diff --git a/packages/rOpenScPCA/R/evaluate-clusters.R b/packages/rOpenScPCA/R/evaluate-clusters.R deleted file mode 100644 index 6ea526584..000000000 --- a/packages/rOpenScPCA/R/evaluate-clusters.R +++ /dev/null @@ -1,246 +0,0 @@ -#' Calculate the silhouette width of clusters -#' -#' This function uses the `bluster::approxSilhouette()` function to calculate the -#' silhouette width for a clustering result. These results can be used downstream to -#' calculate the average silhouette width, a popular metric for cluster evaluation. -#' -#' @param x Either a matrix of principal components (PCs), or a SingleCellExperiment -#' or Seurat object containing PCs. If a matrix is provided, rows should be cells -#' and columns should be PCs, and row names should be cell ids (e.g., barcodes). -#' @param cluster_df A data frame that contains at least the columns `cell_id` and -#' `cluster`. The `cell_id` values should match either the PC matrix row names, -#' or the SingleCellExperiment/Seurat object cell ids. Typically this will be output from -#' the `rOpenScPCA::calculate_clusters()` function. -#' @param pc_name Optionally, the name of the PC matrix in the object. Not used if a -#' matrix is provided. If the name is not provided, the name "PCA" is assumed for -#' SingleCellExperiment objects, and "pca" for Seurat objects. -#' -#' @return Expanded `cluster_df` data frame with these additional columns: -#' - `silhouette_width`, the cell's silhouette width -#' - `other`, the closest cluster other than the one to which the given cell was assigned -#' For more information, see documentation for `bluster::approxSilhouette()` -#' -#' @export -#' @examples -#' \dontrun{ -#' # calculate silhouette width for clusters stored in a data frame -#' cluster_df <- calculate_silhouette(sce_object, cluster_df) -#' } -calculate_silhouette <- function( - x, - cluster_df, - pc_name = NULL) { - x <- prepare_pc_matrix(x, pc_name) - - expected_df_names <- c("cell_id", "cluster") - stopifnot( - "Expected columns 'cell_id' and 'cluster' in the cluster_df." = - all(expected_df_names %in% colnames(cluster_df)) - ) - - silhouette_df <- x |> - bluster::approxSilhouette(cluster_df$cluster) |> - as.data.frame() |> - tibble::rownames_to_column("cell_id") |> - dplyr::rename("silhouette_width" = "width") - - # join with cluster_df in this direction, so that columns in - # cluster_df come first - silhouette_df <- cluster_df |> - dplyr::inner_join(silhouette_df, by = c("cell_id", "cluster")) - - return(silhouette_df) -} - - - - -#' Calculate the neighborhood purity of clusters -#' -#' This function uses the `bluster::neighborPurity()` function to calculate the -#' neighborhood purity values for a clustering result. -#' -#' @param x Either a matrix of principal components (PCs), or a SingleCellExperiment -#' or Seurat object containing PCs. If a matrix is provided, rows should be cells -#' and columns should be PCs, and row names should be cell ids (e.g., barcodes). -#' @param cluster_df A data frame that contains at least the columns `cell_id` and -#' `cluster`. The `cell_id` values should match either the PC matrix row names, -#' or the SingleCellExperiment/Seurat object cell ids. Typically this will be output from -#' the `rOpenScPCA::calculate_clusters()` function. -#' @param pc_name Optionally, the name of the PC matrix in the object. Not used if a -#' matrix is provided. If the name is not provided, the name "PCA" is assumed for -#' SingleCellExperiment objects, and "pca" for Seurat objects. -#' @param ... Additional arguments to pass to `bluster::neighborPurity()` -#' -#' @return Expanded `cluster_df` data frame with these additional columns: -#' - `purity`, the cell's neighborhood purity -#' - `maximum`, the cluster with the highest proportion of observations neighboring the given cell. -#' For more information, see documentation for `bluster::neighborPurity()` -#' -#' @export -#' @examples -#' \dontrun{ -#' # calculate neighborhood purity for clusters stored in a data frame -#' cluster_df <- calculate_purity(sce_object, cluster_df) -#' } -calculate_purity <- function( - x, - cluster_df, - pc_name = NULL, - ...) { - x <- prepare_pc_matrix(x, pc_name) - - expected_df_names <- c("cell_id", "cluster") - stopifnot( - "Expected columns 'cell_id' and 'cluster' in cluster_df." = - all(expected_df_names %in% colnames(cluster_df)) - ) - - purity_df <- x |> - bluster::neighborPurity(cluster_df$cluster) |> - as.data.frame() |> - tibble::rownames_to_column("cell_id") - - # join with cluster_df in this direction, so that columns in - # cluster_df come first - purity_df <- cluster_df |> - dplyr::inner_join(purity_df, by = c("cell_id")) - - return(purity_df) -} - - - -#' Calculate cluster stability using the Adjusted Rand Index (ARI) -#' -#' This function generates and clusters, using provided parameters, bootstrap -#' replicates calculates the Adjusted Rand Index (ARI) between each set of bootstrapped -#' clusters and the original provided clusters. ARI measures similarity between different -#' cluster results, where a value of 0 indicates an entirely random relationship between -#' results, and a value of 1 indicates perfect agreement. -#' -#' When assessing stability, you should specify the same clustering parameters here as -#' were used to calculate the original clusters. -#' -#' Note that this function will also make use of bluster::clusterRows() with the -#' bluster::NNGraphParam() function on a principal components matrix. Note that defaults -#' for some arguments may differ from the bluster::NNGraphParam() defaults. -#' Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme -#' to "jaccard" to align with common practice in scRNA-seq analysis. -#' -#' -#' @param x An object containing PCs that clusters were calculated from. This can be -#' either a SingleCellExperiment object, a Seurat object, or a matrix where columns -#' are PCs and rows are cells. If a matrix is provided, it must have row names of cell -#' ids (e.g., barcodes). -#' @param clusters A vector of cluster ids, typically a numeric factor variable, obtained -#' by previously clustering the PCs. -#' @param replicates Number of bootstrap replicates to perform. Default is 20. -#' @param seed Random seed -#' @param pc_name Optionally, the name of the PC matrix in the object. Not used if a -#' matrix is provided. If the name is not provided, the name "PCA" is assumed for -#' SingleCellExperiment objects, and "pca" for Seurat objects. -#' @param ... Additional arguments to pass to `calculate_clusters()` which calculates -#' bootstrapped clusters. Usually, these will be the same arguments used to generate -#' the original clusters. -#' -#' @return Data frame with columns `replicate` and `ari`, representing the given bootstrap replicate -#' and its ARI value, respectively, and columns representing clustering algorithm parameters which -#' include at least `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also -#' include `resolution`, and leiden clustering will further include `objective_function`. -#' -#' -#' @export -#' -#' @examples -#' \dontrun{ -#' -#' # First, cluster PCs from a SingleCellExperiment object using default parameters -#' # and setting a seed for reproducibility -#' cluster_df <- calculate_clusters(sce_object, seed = 11) -#' # Second, calculate cluster stability using default parameters -#' stability_df <- calculate_stability(sce_object, cluster_df$clusters, seed = 11) -#' -#' -#' # First, cluster PCs from a SingleCellExperiment object using default parameters -#' # and setting a seed for reproducibility -#' cluster_df <- calculate_clusters(sce_object, seed = 11) -#' # Second, calculate cluster stability using default parameters and 50 replicates -#' stability_df <- calculate_stability( -#' sce_object, -#' cluster_df$clusters, -#' replicates = 50, -#' seed = 11 -#' ) -#' -#' -#' # First, cluster PCs from a SingleCellExperiment object using the leiden -#' # algorithm and a resolution of 0.1 -#' cluster_df <- calculate_clusters( -#' sce_object, -#' algorithm = "leiden", -#' resolution = 0.1, -#' seed = 11 -#' ) -#' # Second, calculate cluster stability using the same parameters as were used -#' # for the initial clustering -#' stability_df <- calculate_stability( -#' sce_object, -#' cluster_df$clusters, -#' algorithm = "leiden", -#' resolution = 0.1, -#' seed = 11 -#' ) -#' } -calculate_stability <- function( - x, - clusters, - replicates = 20, - seed = NULL, - pc_name = NULL, - ...) { - if (!is.null(seed)) { - set.seed(seed) - } - - # ensure we have a matrix - pca_matrix <- prepare_pc_matrix(x, pc_name = pc_name) - - # check clusters and matrix compatibility - stopifnot( - "The number of rows in the matrix must equal the length of the clusters vector." = - nrow(pca_matrix) == length(clusters) - ) - - # calculate ARI for each cluster result bootstrap replicate - all_ari_df <- 1:replicates |> - purrr::map( - \(i) { - sample_cells <- sample(nrow(pca_matrix), replace = TRUE) - resampled_pca <- pca_matrix[sample_cells, ] - original_clusters <- clusters[sample_cells] - - resampled_df <- calculate_clusters(resampled_pca, ...) - - ari <- pdfCluster::adj.rand.index(resampled_df$cluster, original_clusters) - - # return df with ari and clustering parameters - ari_df <- resampled_df |> - dplyr::slice(1) |> - dplyr::select(!c("cell_id", "cluster")) |> - dplyr::mutate( - # define this variable here to ensure it's numeric - replicate = i, - ari = ari, - # ensure these columns come first - .before = "algorithm" - ) - - return(ari_df) - } - ) |> - dplyr::bind_rows() - - - return(all_ari_df) -} diff --git a/packages/rOpenScPCA/R/sweep-clusters.R b/packages/rOpenScPCA/R/sweep-clusters.R deleted file mode 100644 index c4fbdb83b..000000000 --- a/packages/rOpenScPCA/R/sweep-clusters.R +++ /dev/null @@ -1,124 +0,0 @@ -#' Calculate clusters across a set of parameters -#' -#' This function can be used to perform reproducible clustering while varying a set of parameters. -#' Multiple values can be provided for any of: -#' - The algorithm (`algorithm`) -#' - The weighting scheme (`weighting`) -#' - Number of nearest neighbors (`nn`) -#' - The resolution parameter (`resolution`) -#' - The objective function parameter (`objective_function`) -#' -#' For each algorithm specified, all parameters possible to use with that -#' algorithm will be systematically varied. This function does not accept additional -#' parameters besides those listed above. -#' Note that defaults for some arguments may differ from the bluster::NNGraphParam() defaults. -#' Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme to "jaccard" -#' to align with common practice in scRNA-seq analysis. -#' -#' @param x An object containing PCs that clustering can be performed in. This can be either -#' a SingleCellExperiment object, a Seurat object, or a matrix where columns are PCs and -#' rows are cells. If a matrix is provided, it must have row names of cell ids (e.g., barcodes). -#' @param algorithm Clustering algorithm to use. Must be one of "louvain" (default), "walktrap", -#' or "leiden". -#' @param weighting Weighting scheme(s) to consider when sweeping parameters. -#' Provide a vector of unique values to vary this parameter. Options include "jaccard" (default), -#' "rank", or "number" -#' @param nn Number of nearest neighbors to consider when sweeping parameters. -#' Provide a vector of unique values to vary this parameter. Default is 10. -#' @param resolution Resolution parameter used by louvain and leiden clustering only. -#' Provide a vector of unique values to vary this parameter. Default is 1. -#' @param objective_function Leiden-specific parameter for whether to use the -#' Constant Potts Model ("CPM"; default) or "modularity". Provide a vector of unique values -#' to vary this parameter. -#' @param seed Random seed to set for clustering. -#' @param threads Number of threads to use. Default is 1. -#' @param pc_name Name of principal components slot in provided object. This argument is only used -#' if a SingleCellExperiment or Seurat object is provided. If not provided, the SingleCellExperiment -#' object name will default to "PCA" and the Seurat object name will default to "pca". -#' -#' @return A list of data frames from performing clustering across all parameter combinations. -#' Columns include `cluster_set` (identifier column for results from a single clustering run), -#' `cell_id`, and `cluster`. Additional columns represent algorithm parameters and include at least: -#' `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also include `resolution`, -#' and leiden clustering will further include `objective_function`. -#' -#' @export -#' -#' @examples -#' \dontrun{ -#' # perform louvain clustering with jaccard weighting (defaults), -#' # varying the nearest neighobor parameter, and set a seed for reproducibility -#' cluster_df <- sweep_clusters( -#' sce_object, -#' nn = c(10, 15, 20, 25), -#' seed = 11 -#' ) -#' -#' # perform louvain clustering, with jaccard and rank weighting, and -#' # varying the nearest neighbor and resolution parameters. -#' cluster_df <- sweep_clusters( -#' sce_object, -#' algorithm = "louvain", -#' weighting = c("jaccard", "rank"), -#' nn = c(10, 15, 20, 25), -#' resolution = c(0.5, 1), -#' seed = 11 -#' ) -#' -#' # perform walktrap and louvain clustering with jaccard weighting, and -#' # varying the nearest neighbors for both algorithms, and resolution for louvain. -#' cluster_df <- sweep_clusters( -#' sce_object, -#' algorithm = c("walktrap", "louvain"), -#' weighting = "jaccard", -#' nn = c(10, 15, 20, 25), -#' resolution = c(0.5, 1), -#' seed = 11 -#' ) -#' } -sweep_clusters <- function( - x, - algorithm = "louvain", - weighting = "jaccard", - nn = 10, - resolution = 1, # louvain or leiden - objective_function = "CPM", # leiden only - threads = 1, - seed = NULL, - pc_name = NULL) { - # check and prepare matrix - pca_matrix <- prepare_pc_matrix(x, pc_name = pc_name) - - # Collect all specific inputs into a single list - sweep_params <- tidyr::expand_grid( - algorithm = unique(algorithm), - weighting = unique(weighting), - nn = unique(nn), - resolution = unique(resolution), - objective_function = unique(objective_function) - ) |> - # set unused parameters for each algorithm to default; this will allow duplicates to be removed by distinct() - dplyr::mutate( - resolution = ifelse(algorithm %in% c("louvain", "leiden"), resolution, 1), - objective_function = ifelse(algorithm == "leiden", objective_function, "CPM") - ) |> - dplyr::distinct() - - sweep_results <- sweep_params |> - purrr::pmap( - \(algorithm, weighting, nn, resolution, objective_function) { - calculate_clusters( - pca_matrix, - algorithm = algorithm, - weighting = weighting, - nn = nn, - resolution = resolution, - objective_function = objective_function, - threads = threads, - seed = seed - ) - } - ) - - return(sweep_results) -} diff --git a/packages/rOpenScPCA/README.md b/packages/rOpenScPCA/README.md deleted file mode 100644 index db420dda3..000000000 --- a/packages/rOpenScPCA/README.md +++ /dev/null @@ -1,26 +0,0 @@ -# rOpenScPCA - -This package contains utility functions to support single-cell RNAseq analysis in the OpenScPCA project. - -## Installation - -`rOpenScPCA` can either be installed with `renv` or the `remotes` package: - -```r -# Install the package with renv -renv::install("AlexsLemonade/OpenScPCA-analysis:packages/rOpenScPCA") -# You can then add to a renv.lock file with renv::snapshot() - -# Install the package with remotes -remotes::install_github("AlexsLemonade/OpenScPCA-analysis/packages/rOpenScPCA") -``` - - diff --git a/packages/rOpenScPCA/man/calculate_clusters.Rd b/packages/rOpenScPCA/man/calculate_clusters.Rd deleted file mode 100644 index 655a36cd1..000000000 --- a/packages/rOpenScPCA/man/calculate_clusters.Rd +++ /dev/null @@ -1,91 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calculate-clusters.R -\name{calculate_clusters} -\alias{calculate_clusters} -\title{Calculate graph-based clusters from a provided matrix} -\usage{ -calculate_clusters( - x, - algorithm = c("louvain", "walktrap", "leiden"), - weighting = c("jaccard", "rank", "number"), - nn = 10, - resolution = 1, - objective_function = c("CPM", "modularity"), - cluster_args = list(), - threads = 1, - seed = NULL, - pc_name = NULL -) -} -\arguments{ -\item{x}{An object containing PCs that clustering can be performed in. This can be either a SingleCellExperiment -object, a Seurat object, or a matrix where columns are PCs and rows are cells. If a matrix is provided, it must -have row names of cell ids (e.g., barcodes).} - -\item{algorithm}{Clustering algorithm to use. Must be one of "louvain" (default), "walktrap", or "leiden".} - -\item{weighting}{Weighting scheme to use. Must be one of "jaccard" (default), "rank", or "number"} - -\item{nn}{Number of nearest neighbors. Default is 10.} - -\item{resolution}{Resolution parameter used by louvain and leiden clustering only. Default is 1.} - -\item{objective_function}{Leiden-specific parameter for whether to use the Constant Potts Model ("CPM"; default) or "modularity"} - -\item{cluster_args}{List of additional arguments to pass to the chosen clustering function. -Only single values for each argument are supported (no vectors or lists). -See igraph documentation for details on each clustering function: https://igraph.org/r/html/latest} - -\item{threads}{Number of threads to use. Default is 1.} - -\item{seed}{Random seed to set for clustering.} - -\item{pc_name}{Name of principal components slot in provided object. This argument is only used if a SingleCellExperiment -or Seurat object is provided. If not provided, the SingleCellExperiment object name will default to "PCA" and the -Seurat object name will default to "pca".} -} -\value{ -A data frame of cluster results with columns `cell_id` and `cluster`. Additional columns represent algorithm parameters - and include at least: `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also include `resolution`, and - leiden clustering will further include `objective_function`. -} -\description{ -This function is provided to simplify application of bluster package clustering functions on OpenScPCA data. -In particular, this function runs bluster::clusterRows() with the bluster::NNGraphParam() function on a -principal components matrix, provided either directly or via single-cell object. -Note that defaults for some arguments may differ from the bluster::NNGraphParam() defaults. -Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme to "jaccard" -to align with common practice in scRNA-seq analysis. -} -\examples{ -\dontrun{ -# cluster PCs from a SingleCellExperiment object using default parameters and -# a random seed for reproducibility -cluster_df <- calculate_clusters(sce_object, seed = 11) - -# cluster PCs from a SingleCellExperiment object using default parameters and 4 threads -cluster_df <- calculate_clusters(sce_object, threads = 4, seed = 11) - -# cluster PCs from a Seurat object using default parameters -cluster_df <- calculate_clusters(seurat_object, seed = 11) - -# cluster directly from a matrix using default parameters -cluster_df <- calculate_clusters(pca_matrix, seed = 11) - -# cluster directly from a matrix using the leiden algorithm with a resolution of 0.1 -cluster_df <- calculate_clusters( - pca_matrix, - algorithm = "leiden", - resolution = 0.1, - seed = 11 -) - -# cluster directly from a matrix using the leiden algorithm with 3 iterations -cluster_df <- calculate_clusters( - pca_matrix, - algorithm = "leiden", - cluster_args = list(n_iterations = 3), - seed = 11 -) -} -} diff --git a/packages/rOpenScPCA/man/calculate_purity.Rd b/packages/rOpenScPCA/man/calculate_purity.Rd deleted file mode 100644 index b9173dcc1..000000000 --- a/packages/rOpenScPCA/man/calculate_purity.Rd +++ /dev/null @@ -1,40 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evaluate-clusters.R -\name{calculate_purity} -\alias{calculate_purity} -\title{Calculate the neighborhood purity of clusters} -\usage{ -calculate_purity(x, cluster_df, pc_name = NULL, ...) -} -\arguments{ -\item{x}{Either a matrix of principal components (PCs), or a SingleCellExperiment -or Seurat object containing PCs. If a matrix is provided, rows should be cells -and columns should be PCs, and row names should be cell ids (e.g., barcodes).} - -\item{cluster_df}{A data frame that contains at least the columns `cell_id` and -`cluster`. The `cell_id` values should match either the PC matrix row names, -or the SingleCellExperiment/Seurat object cell ids. Typically this will be output from -the `rOpenScPCA::calculate_clusters()` function.} - -\item{pc_name}{Optionally, the name of the PC matrix in the object. Not used if a -matrix is provided. If the name is not provided, the name "PCA" is assumed for -SingleCellExperiment objects, and "pca" for Seurat objects.} - -\item{...}{Additional arguments to pass to `bluster::neighborPurity()`} -} -\value{ -Expanded `cluster_df` data frame with these additional columns: -- `purity`, the cell's neighborhood purity -- `maximum`, the cluster with the highest proportion of observations neighboring the given cell. -For more information, see documentation for `bluster::neighborPurity()` -} -\description{ -This function uses the `bluster::neighborPurity()` function to calculate the -neighborhood purity values for a clustering result. -} -\examples{ -\dontrun{ -# calculate neighborhood purity for clusters stored in a data frame -cluster_df <- calculate_purity(sce_object, cluster_df) -} -} diff --git a/packages/rOpenScPCA/man/calculate_silhouette.Rd b/packages/rOpenScPCA/man/calculate_silhouette.Rd deleted file mode 100644 index f3df8e428..000000000 --- a/packages/rOpenScPCA/man/calculate_silhouette.Rd +++ /dev/null @@ -1,39 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evaluate-clusters.R -\name{calculate_silhouette} -\alias{calculate_silhouette} -\title{Calculate the silhouette width of clusters} -\usage{ -calculate_silhouette(x, cluster_df, pc_name = NULL) -} -\arguments{ -\item{x}{Either a matrix of principal components (PCs), or a SingleCellExperiment -or Seurat object containing PCs. If a matrix is provided, rows should be cells -and columns should be PCs, and row names should be cell ids (e.g., barcodes).} - -\item{cluster_df}{A data frame that contains at least the columns `cell_id` and -`cluster`. The `cell_id` values should match either the PC matrix row names, -or the SingleCellExperiment/Seurat object cell ids. Typically this will be output from -the `rOpenScPCA::calculate_clusters()` function.} - -\item{pc_name}{Optionally, the name of the PC matrix in the object. Not used if a -matrix is provided. If the name is not provided, the name "PCA" is assumed for -SingleCellExperiment objects, and "pca" for Seurat objects.} -} -\value{ -Expanded `cluster_df` data frame with these additional columns: -- `silhouette_width`, the cell's silhouette width -- `other`, the closest cluster other than the one to which the given cell was assigned -For more information, see documentation for `bluster::approxSilhouette()` -} -\description{ -This function uses the `bluster::approxSilhouette()` function to calculate the -silhouette width for a clustering result. These results can be used downstream to -calculate the average silhouette width, a popular metric for cluster evaluation. -} -\examples{ -\dontrun{ -# calculate silhouette width for clusters stored in a data frame -cluster_df <- calculate_silhouette(sce_object, cluster_df) -} -} diff --git a/packages/rOpenScPCA/man/calculate_stability.Rd b/packages/rOpenScPCA/man/calculate_stability.Rd deleted file mode 100644 index 24ddde983..000000000 --- a/packages/rOpenScPCA/man/calculate_stability.Rd +++ /dev/null @@ -1,100 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/evaluate-clusters.R -\name{calculate_stability} -\alias{calculate_stability} -\title{Calculate cluster stability using the Adjusted Rand Index (ARI)} -\usage{ -calculate_stability( - x, - clusters, - replicates = 20, - seed = NULL, - pc_name = NULL, - ... -) -} -\arguments{ -\item{x}{An object containing PCs that clusters were calculated from. This can be -either a SingleCellExperiment object, a Seurat object, or a matrix where columns -are PCs and rows are cells. If a matrix is provided, it must have row names of cell -ids (e.g., barcodes).} - -\item{clusters}{A vector of cluster ids, typically a numeric factor variable, obtained -by previously clustering the PCs.} - -\item{replicates}{Number of bootstrap replicates to perform. Default is 20.} - -\item{seed}{Random seed} - -\item{pc_name}{Optionally, the name of the PC matrix in the object. Not used if a -matrix is provided. If the name is not provided, the name "PCA" is assumed for -SingleCellExperiment objects, and "pca" for Seurat objects.} - -\item{...}{Additional arguments to pass to `calculate_clusters()` which calculates -bootstrapped clusters. Usually, these will be the same arguments used to generate -the original clusters.} -} -\value{ -Data frame with columns `replicate` and `ari`, representing the given bootstrap replicate - and its ARI value, respectively, and columns representing clustering algorithm parameters which - include at least `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also - include `resolution`, and leiden clustering will further include `objective_function`. -} -\description{ -This function generates and clusters, using provided parameters, bootstrap -replicates calculates the Adjusted Rand Index (ARI) between each set of bootstrapped -clusters and the original provided clusters. ARI measures similarity between different -cluster results, where a value of 0 indicates an entirely random relationship between -results, and a value of 1 indicates perfect agreement. -} -\details{ -When assessing stability, you should specify the same clustering parameters here as -were used to calculate the original clusters. - -Note that this function will also make use of bluster::clusterRows() with the -bluster::NNGraphParam() function on a principal components matrix. Note that defaults -for some arguments may differ from the bluster::NNGraphParam() defaults. -Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme -to "jaccard" to align with common practice in scRNA-seq analysis. -} -\examples{ -\dontrun{ - -# First, cluster PCs from a SingleCellExperiment object using default parameters -# and setting a seed for reproducibility -cluster_df <- calculate_clusters(sce_object, seed = 11) -# Second, calculate cluster stability using default parameters -stability_df <- calculate_stability(sce_object, cluster_df$clusters, seed = 11) - - -# First, cluster PCs from a SingleCellExperiment object using default parameters -# and setting a seed for reproducibility -cluster_df <- calculate_clusters(sce_object, seed = 11) -# Second, calculate cluster stability using default parameters and 50 replicates -stability_df <- calculate_stability( - sce_object, - cluster_df$clusters, - replicates = 50, - seed = 11 -) - - -# First, cluster PCs from a SingleCellExperiment object using the leiden -# algorithm and a resolution of 0.1 -cluster_df <- calculate_clusters( - sce_object, - algorithm = "leiden", - resolution = 0.1, - seed = 11 -) -# Second, calculate cluster stability using the same parameters as were used -# for the initial clustering -stability_df <- calculate_stability( - sce_object, - cluster_df$clusters, - algorithm = "leiden", - resolution = 0.1, - seed = 11 -) -} -} diff --git a/packages/rOpenScPCA/man/extract_pc_matrix.Rd b/packages/rOpenScPCA/man/extract_pc_matrix.Rd deleted file mode 100644 index 8f21bc930..000000000 --- a/packages/rOpenScPCA/man/extract_pc_matrix.Rd +++ /dev/null @@ -1,37 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calculate-clusters.R -\name{extract_pc_matrix} -\alias{extract_pc_matrix} -\title{Extract a principal components (PC) matrix from either a SingleCellExperiment -or a Seurat object.} -\usage{ -extract_pc_matrix(sc_object, pc_name = NULL) -} -\arguments{ -\item{sc_object}{Either a SingleCellExperiment or Seurat object} - -\item{pc_name}{Optionally, the name of the PC matrix in the object. If this is -not provided, the name "PCA" is assumed for SingleCellExperiment objects, and -"pca" for Seurat objects.} -} -\value{ -PC matrix with row names -} -\description{ -This function first determines if the provided object is a SingleCellExperiment or -Seurat object, and then extract the PC matrix. If no name for the PC matrix is provided, -this function will assume the name of "PCA" for SingleCellExperiment objects, and -"pca" for Seurat objects. -} -\examples{ -\dontrun{ -# extract PC matrix from SCE object, assuming default name "PCA" -pca_matrix <- extract_pc_matrix(sce_object) - -# extract PC matrix from SCE object with non-default name "PCA_MAT" -pca_matrix <- extract_pc_matrix(sce_object, pc_name = "PCA_MAT") - -# extract PC matrix from Seurat object, assuming default name "pca" -pca_matrix <- extract_pc_matrix(seurat_object) -} -} diff --git a/packages/rOpenScPCA/man/prepare_pc_matrix.Rd b/packages/rOpenScPCA/man/prepare_pc_matrix.Rd deleted file mode 100644 index 9c4eadc07..000000000 --- a/packages/rOpenScPCA/man/prepare_pc_matrix.Rd +++ /dev/null @@ -1,23 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/calculate-clusters.R -\name{prepare_pc_matrix} -\alias{prepare_pc_matrix} -\title{Helper function to check and/or extract a matrix of PCs from a given object} -\usage{ -prepare_pc_matrix(x, pc_name = NULL) -} -\arguments{ -\item{x}{Either a matrix of principal components (PCs), or a SingleCellExperiment -or Seurat object containing PCs. If a matrix is provided, rows should be cells -and columns should be PCs, and row names should be cell ids (e.g., barcodes).} - -\item{pc_name}{Optionally, the name of the PC matrix in the object. Not used for -matrices. If this is not provided, the name "PCA" is assumed for -SingleCellExperiment objects, and "pca" for Seurat objects.} -} -\value{ -A matrix of PCs with row names representing cell ids -} -\description{ -Helper function to check and/or extract a matrix of PCs from a given object -} diff --git a/packages/rOpenScPCA/man/sweep_clusters.Rd b/packages/rOpenScPCA/man/sweep_clusters.Rd deleted file mode 100644 index 3286d3ed3..000000000 --- a/packages/rOpenScPCA/man/sweep_clusters.Rd +++ /dev/null @@ -1,105 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/sweep-clusters.R -\name{sweep_clusters} -\alias{sweep_clusters} -\title{Calculate clusters across a set of parameters} -\usage{ -sweep_clusters( - x, - algorithm = "louvain", - weighting = "jaccard", - nn = 10, - resolution = 1, - objective_function = "CPM", - threads = 1, - seed = NULL, - pc_name = NULL -) -} -\arguments{ -\item{x}{An object containing PCs that clustering can be performed in. This can be either -a SingleCellExperiment object, a Seurat object, or a matrix where columns are PCs and -rows are cells. If a matrix is provided, it must have row names of cell ids (e.g., barcodes).} - -\item{algorithm}{Clustering algorithm to use. Must be one of "louvain" (default), "walktrap", -or "leiden".} - -\item{weighting}{Weighting scheme(s) to consider when sweeping parameters. -Provide a vector of unique values to vary this parameter. Options include "jaccard" (default), - "rank", or "number"} - -\item{nn}{Number of nearest neighbors to consider when sweeping parameters. -Provide a vector of unique values to vary this parameter. Default is 10.} - -\item{resolution}{Resolution parameter used by louvain and leiden clustering only. -Provide a vector of unique values to vary this parameter. Default is 1.} - -\item{objective_function}{Leiden-specific parameter for whether to use the -Constant Potts Model ("CPM"; default) or "modularity". Provide a vector of unique values -to vary this parameter.} - -\item{threads}{Number of threads to use. Default is 1.} - -\item{seed}{Random seed to set for clustering.} - -\item{pc_name}{Name of principal components slot in provided object. This argument is only used -if a SingleCellExperiment or Seurat object is provided. If not provided, the SingleCellExperiment -object name will default to "PCA" and the Seurat object name will default to "pca".} -} -\value{ -A list of data frames from performing clustering across all parameter combinations. - Columns include `cluster_set` (identifier column for results from a single clustering run), - `cell_id`, and `cluster`. Additional columns represent algorithm parameters and include at least: - `algorithm`, `weighting`, and `nn`. Louvain and leiden clustering will also include `resolution`, - and leiden clustering will further include `objective_function`. -} -\description{ -This function can be used to perform reproducible clustering while varying a set of parameters. -Multiple values can be provided for any of: - - The algorithm (`algorithm`) - - The weighting scheme (`weighting`) - - Number of nearest neighbors (`nn`) - - The resolution parameter (`resolution`) - - The objective function parameter (`objective_function`) -} -\details{ -For each algorithm specified, all parameters possible to use with that -algorithm will be systematically varied. This function does not accept additional -parameters besides those listed above. -Note that defaults for some arguments may differ from the bluster::NNGraphParam() defaults. -Specifically, the clustering algorithm defaults to "louvain" and the weighting scheme to "jaccard" -to align with common practice in scRNA-seq analysis. -} -\examples{ -\dontrun{ -# perform louvain clustering with jaccard weighting (defaults), -# varying the nearest neighobor parameter, and set a seed for reproducibility -cluster_df <- sweep_clusters( - sce_object, - nn = c(10, 15, 20, 25), - seed = 11 -) - -# perform louvain clustering, with jaccard and rank weighting, and -# varying the nearest neighbor and resolution parameters. -cluster_df <- sweep_clusters( - sce_object, - algorithm = "louvain", - weighting = c("jaccard", "rank"), - nn = c(10, 15, 20, 25), - resolution = c(0.5, 1), - seed = 11 -) - -# perform walktrap and louvain clustering with jaccard weighting, and -# varying the nearest neighbors for both algorithms, and resolution for louvain. -cluster_df <- sweep_clusters( - sce_object, - algorithm = c("walktrap", "louvain"), - weighting = "jaccard", - nn = c(10, 15, 20, 25), - resolution = c(0.5, 1), - seed = 11 -) -} -} diff --git a/packages/rOpenScPCA/rOpenScPCA.Rproj b/packages/rOpenScPCA/rOpenScPCA.Rproj deleted file mode 100644 index ba381fb1b..000000000 --- a/packages/rOpenScPCA/rOpenScPCA.Rproj +++ /dev/null @@ -1,20 +0,0 @@ -Version: 1.0 - -RestoreWorkspace: No -SaveWorkspace: No -AlwaysSaveHistory: No - -EnableCodeIndexing: Yes -UseSpacesForTab: Yes -NumSpacesForTab: 2 -Encoding: UTF-8 - -RnwWeave: Sweave -LaTeX: pdfLaTeX - -AutoAppendNewline: Yes -StripTrailingWhitespace: Yes - -BuildType: Package -PackageUseDevtools: Yes -PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/packages/rOpenScPCA/tests/testthat.R b/packages/rOpenScPCA/tests/testthat.R deleted file mode 100644 index 5dc4da8e3..000000000 --- a/packages/rOpenScPCA/tests/testthat.R +++ /dev/null @@ -1,12 +0,0 @@ -# This file is part of the standard setup for testthat. -# It is recommended that you do not modify it. -# -# Where should you do additional test configuration? -# Learn more about the roles of various files in: -# * https://r-pkgs.org/testing-design.html#sec-tests-files-overview -# * https://testthat.r-lib.org/articles/special-files.html - -library(testthat) -library(rOpenScPCA) - -test_check("rOpenScPCA") diff --git a/packages/rOpenScPCA/tests/testthat/test-calculate-clusters.R b/packages/rOpenScPCA/tests/testthat/test-calculate-clusters.R deleted file mode 100644 index ef977f8ff..000000000 --- a/packages/rOpenScPCA/tests/testthat/test-calculate-clusters.R +++ /dev/null @@ -1,147 +0,0 @@ -suppressPackageStartupMessages(library(SingleCellExperiment)) - -set.seed(2024) -sce <- splatter::simpleSimulate(nGenes = 1000, verbose = FALSE) |> - scater::logNormCounts() |> - scater::runPCA(ncomponents = 10) - -test_mat <- reducedDim(sce, "PCA") - -srat <- Seurat::CreateSeuratObject(counts = counts(sce), assay = "RNA") -srat[["pca"]] <- Seurat::CreateDimReducObject( - embeddings = test_mat, - key = "PC_", # underscore avoids Seurat warning that it's adding an underscore - assay = "RNA" -) - -test_that("calculate_clusters runs with a matrix, defaults", { - cluster_df <- calculate_clusters(test_mat) - - expect_setequal( - colnames(cluster_df), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution") - ) - expect_equal(cluster_df$cell_id, rownames(test_mat)) - expect_s3_class(cluster_df$cluster, "factor") - expect_equal(unique(cluster_df$algorithm), "louvain") - expect_equal(unique(cluster_df$weighting), "jaccard") - expect_equal(unique(cluster_df$nn), 10) - expect_equal(unique(cluster_df$resolution), 1) -}) - - -test_that("calculate_clusters runs with additional cluster_args", { - cluster_df <- calculate_clusters( - test_mat, - algorithm = "leiden", - cluster_args = list(n_iterations = 3) - ) - - expect_setequal( - colnames(cluster_df), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution", "objective_function", "n_iterations") - ) - expect_equal(unique(cluster_df$n_iterations), 3) -}) - - - -test_that("calculate_clusters runs when cluster_args is empty", { - cluster_df <- calculate_clusters( - test_mat, - algorithm = "walktrap" - ) - - expect_setequal( - colnames(cluster_df), - c("cell_id", "cluster", "algorithm", "weighting", "nn") - ) - expect_equal(unique(cluster_df$algorithm), "walktrap") -}) - - -test_that("calculate_clusters runs with an object, defaults", { - cluster_df_sce <- calculate_clusters(sce) - expect_setequal( - colnames(cluster_df_sce), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution") - ) - expect_equal(cluster_df_sce$cell_id, rownames(test_mat)) - - cluster_df_srat <- calculate_clusters(srat) - expect_setequal( - colnames(cluster_df_srat), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution") - ) - expect_equal(cluster_df_srat$cell_id, rownames(test_mat)) -}) - - - -test_that("calculate_clusters errors as expected", { - expect_error(calculate_clusters(test_mat, resolution = "string")) - expect_error(calculate_clusters(test_mat, nn = "string")) - expect_error( - calculate_clusters( - test_mat, - cluster_args = list(too_long = 1:10) - ) - ) -}) - - - -test_that("extract_pc_matrix works as expected", { - pc_mat_sce <- extract_pc_matrix(sce) - expect_identical( - pc_mat_sce, - test_mat - ) - - pc_mat_srt <- extract_pc_matrix(srat) - # update test_mat column names to match what will have Seurat changed them to - colnames(test_mat) <- gsub("^PC", "PC_", colnames(test_mat)) - expect_identical(pc_mat_srt, test_mat) -}) - -test_that("extract_pc_matrix errors as expected", { - expect_error( - extract_pc_matrix(sce, pc_name = "bad_name") - ) - expect_error( - extract_pc_matrix(srat, pc_name = "bad_name") - ) - expect_error( - extract_pc_matrix(test_mat) - ) -}) - - - - -test_that("prepare_pc_matrix works as expected with matrix input", { - mat <- prepare_pc_matrix(test_mat) - expect_identical(mat, test_mat) -}) - - -test_that("prepare_pc_matrix works as expected with SCE input", { - mat <- prepare_pc_matrix(sce) - expect_identical(mat, test_mat) -}) - -test_that("prepare_pc_matrix works as expected with Seurat input", { - mat <- prepare_pc_matrix(srat) - # update test_mat column names to match what Seurat will have changed them to - colnames(test_mat) <- gsub("^PC", "PC_", colnames(test_mat)) - expect_identical(mat, test_mat) -}) - - -test_that("prepare_pc_matrix fails as expected ", { - test_mat_nonames <- test_mat - rownames(test_mat_nonames) <- NULL - - expect_error(calculate_clusters(test_mat_nonames)) - expect_error(calculate_clusters("not a matrix")) -}) diff --git a/packages/rOpenScPCA/tests/testthat/test-evaluate-clusters.R b/packages/rOpenScPCA/tests/testthat/test-evaluate-clusters.R deleted file mode 100644 index 83da7ae56..000000000 --- a/packages/rOpenScPCA/tests/testthat/test-evaluate-clusters.R +++ /dev/null @@ -1,100 +0,0 @@ -suppressPackageStartupMessages(library(SingleCellExperiment)) - -set.seed(2024) -sce <- splatter::simpleSimulate(nGenes = 1000, verbose = FALSE) |> - scater::logNormCounts() |> - scater::runPCA(ncomponents = 10) -test_mat <- reducedDim(sce, "PCA") - - -cluster_df <- calculate_clusters(test_mat) - -test_that("calculate_silhouette works as expected", { - df <- calculate_silhouette(test_mat, cluster_df) - - expect_setequal( - colnames(df), - c(colnames(cluster_df), "silhouette_width", "other") - ) - expect_equal(df$cell_id, rownames(test_mat)) - expect_equal(df$cluster, cluster_df$cluster) - expect_vector(df$silhouette_width, ptype = numeric()) - expect_s3_class(df$other, "factor") -}) - - - -test_that("calculate_purity works as expected", { - df <- calculate_purity(test_mat, cluster_df) - - expect_setequal( - colnames(df), - c(colnames(cluster_df), "purity", "maximum") - ) - expect_equal(df$cell_id, rownames(test_mat)) - expect_equal(df$cluster, cluster_df$cluster) - expect_vector(df$purity, ptype = numeric()) - expect_s3_class(df$maximum, "factor") -}) - - - - - -test_that("calculate_stability works as expected with defaults", { - # note that we suppress warnings since this calculation done on fake - # test data gives expected warnings about ties during the ARI calculation. - suppressWarnings({ - df <- calculate_stability(test_mat, cluster_df$cluster) - }) - - expected_names <- colnames(cluster_df)[!(colnames(cluster_df) %in% c("cell_id", "cluster"))] - expect_setequal( - colnames(df), - c("replicate", "ari", expected_names) - ) - expect_equal(df$replicate, 1:20) # checks rows too - expect_vector(df$ari, ptype = numeric()) -}) - - -test_that("calculate_stability works as expected with different replicates", { - # note that we suppress warnings since this calculation done on fake - # test data gives expected warnings about ties during the ARI calculation. - suppressWarnings({ - df <- calculate_stability(test_mat, cluster_df$cluster, replicates = 2) - }) - expect_equal(nrow(df), 2) -}) - - - -test_that("calculate_stability works as expected with object and pc_name", { - reducedDimNames(sce) <- "my_pca" - - # note that we suppress warnings since this calculation done on fake - # test data gives expected warnings about ties during the ARI calculation. - suppressWarnings({ - df <- calculate_stability( - sce, - cluster_df$cluster, - replicates = 2, - pc_name = "my_pca" - ) - }) - expect_equal(nrow(df), 2) -}) - - - -test_that("calculate_stability errors as expected", { - expect_error({ - # mismatched cluster vector length - calculate_stability(test_mat, cluster_df$cluster[1:5]) - }) - - expect_error({ - # cluster_df not a vector - calculate_stability(test_mat, cluster_df) - }) -}) diff --git a/packages/rOpenScPCA/tests/testthat/test-sweep-clusters.R b/packages/rOpenScPCA/tests/testthat/test-sweep-clusters.R deleted file mode 100644 index 4011b88b3..000000000 --- a/packages/rOpenScPCA/tests/testthat/test-sweep-clusters.R +++ /dev/null @@ -1,127 +0,0 @@ -suppressPackageStartupMessages(library(SingleCellExperiment)) - -set.seed(2024) -sce <- splatter::simpleSimulate(nGenes = 1000, verbose = FALSE) |> - scater::logNormCounts() |> - scater::runPCA(ncomponents = 10) - -test_mat <- reducedDim(sce, "PCA") - -srat <- Seurat::CreateSeuratObject(counts = counts(sce), assay = "RNA") -srat[["pca"]] <- Seurat::CreateDimReducObject( - embeddings = test_mat, - key = "PC_", # underscore avoids Seurat warning that it's adding an underscore - assay = "RNA" -) - -test_that("sweep_clusters works as expected with default algorithm & weighting", { - sweep_list <- sweep_clusters( - test_mat, - nn = c(10, 15), - resolution = c(0.5, 1) - ) - - expect_length(sweep_list, 4) - - sweep_list |> - purrr::walk( - \(df) { - expect_setequal( - colnames(df), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution") - ) - - # these tests confirm the defaults went through - expect_equal(unique(df$algorithm), "louvain") - expect_equal(unique(df$weighting), "jaccard") - - expect_true( - all(df$nn == 10) || all(df$nn == 15) - ) - expect_true( - all(df$resolution == 0.5) || all(df$resolution == 1) - ) - } - ) -}) - - -test_that("sweep_clusters works as expected with matrix input", { - sweep_list <- sweep_clusters(test_mat) - expect_length(sweep_list, 1) -}) - - -test_that("sweep_clusters works as expected with Seurat input", { - sweep_list <- sweep_clusters(srat) - expect_length(sweep_list, 1) -}) - - - -test_that("sweep_clusters works as expected with non-default algorithm", { - sweep_list <- sweep_clusters( - test_mat, - algorithm = "leiden", - objective_function = "modularity", - resolution = c(0.5, 1) - ) - - sweep_list |> - purrr::walk( - \(df) { - expect_setequal( - colnames(df), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution", "objective_function") - ) - - expect_equal(unique(df$algorithm), "leiden") - expect_equal(unique(df$objective_function), "modularity") - - expect_true( - all(df$resolution == 0.5) || all(df$resolution == 1) - ) - } - ) -}) - - - - -test_that("sweep_clusters works as expected with multiple algorithms", { - sweep_list <- sweep_clusters( - test_mat, - algorithm = c("walktrap", "louvain"), - # used by both - nn = c(10, 15), - # only used by louvain - resolution = c(0.5, 1) - ) - - # count algorithms - alg_counts <- sweep_list |> - purrr::map(\(df) unique(df$algorithm)) |> - purrr::reduce(c) - expect_length(alg_counts, 6) - expect_equal(sum(alg_counts == "louvain"), 4) - expect_equal(sum(alg_counts == "walktrap"), 2) - - - - sweep_list |> - purrr::walk( - \(df) { - if (unique(df$algorithm) == "walktrap") { - expect_setequal( - colnames(df), - c("cell_id", "cluster", "algorithm", "weighting", "nn") - ) - } else if (unique(df$algorithm) == "louvain") { - expect_setequal( - colnames(df), - c("cell_id", "cluster", "algorithm", "weighting", "nn", "resolution") - ) - } - } - ) -})