Auto-style code

LieberInstitute · Oct 24, 2024 · 15b6708 · 15b6708
1 parent c117e6c
commit 15b6708
Show file tree

Hide file tree

Showing 11 changed files with 74 additions and 75 deletions.
diff --git a/R/add_array_coords.R b/R/add_array_coords.R
@@ -63,7 +63,7 @@
 #' ########################################################################
 #' #   Prepare sample_info
 #' ########################################################################
-#' 
+#'
 #' sample_info <- dplyr::tibble(
 #'     group = "Br2719",
 #'     capture_area = c("V13B23-283_A1", "V13B23-283_C1", "V13B23-283_D1")
@@ -77,7 +77,7 @@
 #' sample_info$spaceranger_dir <- file.path(
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
-#' 
+#'
 #' #   Add Fiji-output-related columns
 #' fiji_dir <- tempdir()
 #' temp <- unzip(
@@ -86,7 +86,7 @@
 #' )
 #' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
 #' sample_info$fiji_image_path <- temp[grep("png$", temp)]
-#' 
+#'
 #' ## Re-size images and add more information to the sample_info
 #' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'

diff --git a/R/array_error_metrics.R b/R/array_error_metrics.R
@@ -100,7 +100,7 @@
 .add_error_metrics <- function(coords, coords_new, inter_spot_dist_px) {
     ## For R CMD CHECK
     pxl_col_in_fullres <- pxl_col_in_fullres_rounded <- pxl_row_in_fullres <- pxl_row_in_fullres_rounded <- key <- capture_area <- NULL
-    
+
     coords_new <- coords_new |>
         mutate(
             euclidean_error = (

diff --git a/R/as.Seurat.R b/R/as.Seurat.R
@@ -50,18 +50,17 @@
 #'
 #' ## Let's look at our resulting Seurat object
 #' seur_stitched
-as.Seurat <- function(
-        spe,
-        spatial_cols = c(
-            "tissue" = "in_tissue",
-            "row" = "array_row",
-            "col" = "array_col",
-            "imagerow" = "pxl_row_in_fullres",
-            "imagecol" = "pxl_col_in_fullres"
-        ),
-        verbose = TRUE) {
+as.Seurat <- function(spe,
+    spatial_cols = c(
+        "tissue" = "in_tissue",
+        "row" = "array_row",
+        "col" = "array_col",
+        "imagerow" = "pxl_row_in_fullres",
+        "imagecol" = "pxl_col_in_fullres"
+    ),
+    verbose = TRUE) {
     #   Seurat is only suggested
-    BiocBaseUtils::checkInstalled('Seurat')
+    BiocBaseUtils::checkInstalled("Seurat")
 
     SPOT_DIAMETER <- 55e-6
 

diff --git a/R/build_SpatialExperiment.R b/R/build_SpatialExperiment.R
@@ -52,7 +52,7 @@
 #' sample_info$spaceranger_dir <- file.path(
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
-#' 
+#'
 #' #   Add Fiji-output-related columns
 #' fiji_dir <- tempdir()
 #' temp <- unzip(
@@ -61,7 +61,7 @@
 #' )
 #' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
 #' sample_info$fiji_image_path <- temp[grep("png$", temp)]
-#' 
+#'
 #' ## Re-size images and add more information to the sample_info
 #' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'

diff --git a/R/merge_overlapping.R b/R/merge_overlapping.R
@@ -4,7 +4,7 @@
 #' merge overlapping (same array coordinates) spots by adding
 #' expression (i.e. from \code{assays(spe)$counts}), returning a
 #' \code{SpatialExperiment} with at most one spot per array location.
-#' 
+#'
 #' \code{colData(spe)} and \code{spatialCoords(spe)} of the merged spots are
 #' taken from the spots whose \code{exclude_overlapping} values are \code{TRUE}.
 #'
@@ -27,77 +27,77 @@
 #' if (!exists("spe")) {
 #'     spe <- spatialLIBD::fetch_data(type = "visiumStitched_brain_spe")
 #' }
-#' 
+#'
 #' #   Group colData by group and array coordinates
-#' grouped_coldata = colData(spe) |>
+#' grouped_coldata <- colData(spe) |>
 #'     dplyr::as_tibble() |>
 #'     dplyr::group_by(group, array_row, array_col)
-#' 
+#'
 #' #   Find the first 100 keys that overlap other spots and don't, respectively
-#' overlapping_keys = grouped_coldata |>
+#' overlapping_keys <- grouped_coldata |>
 #'     dplyr::filter(dplyr::n() > 1) |>
 #'     dplyr::slice_head(n = 2) |>
 #'     dplyr::ungroup() |>
 #'     dplyr::slice_head(n = 100) |>
 #'     dplyr::pull(key)
-#' nonoverlapping_keys = grouped_coldata |>
+#' nonoverlapping_keys <- grouped_coldata |>
 #'     dplyr::filter(dplyr::n() == 1) |>
 #'     dplyr::ungroup() |>
 #'     dplyr::slice_head(n = 100) |>
 #'     dplyr::pull(key)
-#' 
+#'
 #' #   Built a small SPE containing some overlaps and some non-overlapping spots
-#' small_spe = spe[, c(overlapping_keys, nonoverlapping_keys)]
-#' 
+#' small_spe <- spe[, c(overlapping_keys, nonoverlapping_keys)]
+#'
 #' #   Merge overlapping spots
-#' small_spe_merged = merge_overlapping(small_spe)
-#' 
+#' small_spe_merged <- merge_overlapping(small_spe)
+#'
 #' #   All array coordinates have just one unique spot after merging
 #' colData(small_spe_merged) |>
 #'     dplyr::as_tibble() |>
 #'     dplyr::group_by(group, array_row, array_col) |>
 #'     dplyr::summarize(n = dplyr::n()) |>
 #'     dplyr::pull(n) |>
 #'     table()
-#' 
+#'
 merge_overlapping <- function(spe) {
     ## For R CMD CHECK
-    array_row = array_col = exclude_overlapping = gene_id = group = key = NULL
+    array_row <- array_col <- exclude_overlapping <- gene_id <- group <- key <- NULL
 
     #   Find keys corresponding to spots that must be merged
-    overlapping_keys = colData(spe) |>
+    overlapping_keys <- colData(spe) |>
         as_tibble() |>
         group_by(group, array_row, array_col) |>
         filter(n() > 1) |>
         pull(key)
-    
+
     #   Nothing to merge
-    if(length(overlapping_keys) == 0) {
+    if (length(overlapping_keys) == 0) {
         return(spe)
     }
 
     #   Check assays and halt or warn
-    if (!('counts' %in% names(assays(spe)))) {
+    if (!("counts" %in% names(assays(spe)))) {
         stop("'counts' assay missing; unable to merge overlapping spots.")
     }
     if (length(assays(spe)) > 1) {
         warning("Dropping assays other than 'counts' for merging.")
     }
-    assays(spe) = list(counts = assays(spe)$counts)
+    assays(spe) <- list(counts = assays(spe)$counts)
 
-    if(length(reducedDims(spe)) > 0) {
+    if (length(reducedDims(spe)) > 0) {
         warning("Dropped reducedDims(spe) for merging")
     }
-    reducedDims(spe) = list()
-    
-    merged_data =
+    reducedDims(spe) <- list()
+
+    merged_data <-
         #   For the spots to merge, form a tibble with spots as rows and genes
         #   and colData() columns as columns
-        assays(spe)$counts[,match(overlapping_keys, spe$key)] |>
+        assays(spe)$counts[, match(overlapping_keys, spe$key)] |>
         as.matrix() |>
         t() |>
         cbind(
-            colData(spe[,match(overlapping_keys, spe$key)]) |>
+            colData(spe[, match(overlapping_keys, spe$key)]) |>
                 as_tibble()
         ) |>
         as_tibble() |>
@@ -112,10 +112,10 @@ merge_overlapping <- function(spe) {
         ungroup() |>
         #   Now just take colData variables from the non-excluded spot
         filter(exclude_overlapping) |>
-        tidyr::pivot_wider(names_from = 'gene_id', values_from = 'expression')
-    
+        tidyr::pivot_wider(names_from = "gene_id", values_from = "expression")
+
     #   Construct the SPE for just the merged spots
-    spe_overlap = SpatialExperiment(
+    spe_overlap <- SpatialExperiment(
         assays = list(
             counts = merged_data |>
                 select(!colnames(colData(spe))) |>
@@ -125,12 +125,12 @@ merge_overlapping <- function(spe) {
         colData = merged_data |>
             select(colnames(colData(spe))),
         rowData = rowData(spe),
-        spatialCoords = spatialCoords(spe)[match(merged_data$key, spe$key),]
+        spatialCoords = spatialCoords(spe)[match(merged_data$key, spe$key), ]
     )
-    colnames(spe_overlap) = spe_overlap$key
+    colnames(spe_overlap) <- spe_overlap$key
 
     #   Combined the merged spots and non-overlapping spots
-    spe = BiocGenerics::cbind(spe_overlap, spe[, !(spe$key %in% overlapping_keys)])
-    
+    spe <- BiocGenerics::cbind(spe_overlap, spe[, !(spe$key %in% overlapping_keys)])
+
     return(spe)
 }
diff --git a/R/prep_fiji.R b/R/prep_fiji.R
@@ -6,15 +6,15 @@
 #' in particular, `tissue_positions.csv`, `tissue_lowres_image.png`, and
 #' `scalefactors_json.json` files are created. These functions are necessary to
 #' run in preparation for \code{build_SpatialExperiment()}.
-#' 
+#'
 #' Given a `data.frame()` of sample information (\code{sample_info}) with
 #' columns \code{capture_area}, \code{group}, and \code{fiji_xml_path},
 #' expected to have one unique path to Fiji XML output per group, `prep_fiji_coords`
 #' reads in the pixel coordinates from each capture area's \code{tissue_positions.csv}
 #' file from SpaceRanger, and transform using the rotation matrix specified
 #' by Fiji <https://imagej.net/software/fiji/>. It writes one new \code{tissue_positions.csv}
 #' file per group.
-#' 
+#'
 #' After stitching all groups in \code{sample_info} with Fiji, images of
 #' various resolutions (pixel dimensions) are left. `prep_fiji_image()` creates copies
 #' of each image whose largest dimension is \code{lowres_max_size} pixels. It
@@ -52,7 +52,7 @@
 #' sample_info$spaceranger_dir <- file.path(
 #'     sr_dir, sample_info$capture_area, "outs", "spatial"
 #' )
-#' 
+#'
 #' #   Add Fiji-output-related columns
 #' fiji_dir <- tempdir()
 #' temp <- unzip(
@@ -61,7 +61,7 @@
 #' )
 #' sample_info$fiji_xml_path <- temp[grep("xml$", temp)]
 #' sample_info$fiji_image_path <- temp[grep("png$", temp)]
-#' 
+#'
 #' ## Re-size images and add more information to the sample_info
 #' sample_info <- rescale_fiji_inputs(sample_info, out_dir = tempdir())
 #'
@@ -83,7 +83,7 @@
 #'
 #' #    'prep_fiji_image' produced an image and scalefactors
 #' out_paths_image
-#' 
+#'
 #' #    'prep_fiji_coords' produced a file of spatial coordinates for the
 #' #    stitched Br2719
 #' readr::read_csv(out_path_coords)
@@ -177,7 +177,7 @@ prep_fiji_image <- function(sample_info, out_dir, lowres_max_size = 1200) {
 }
 
 #' @describeIn prep_fiji Apply transform info from Fiji XML output
-#' 
+#'
 #' @import xml2
 #' @importFrom stringr str_replace_all str_detect
 #' @importFrom readr read_csv write_csv

diff --git a/R/rescale_fiji_inputs.R b/R/rescale_fiji_inputs.R
@@ -83,7 +83,7 @@ rescale_fiji_inputs <- function(sample_info, out_dir) {
             )
         )
     }
-    
+
     if (!dir.exists(out_dir)) {
         stop("'out_dir' does not exist; please create it.")
     }

diff --git a/tests/testthat/test-array_error_metrics.R b/tests/testthat/test-array_error_metrics.R
@@ -8,7 +8,7 @@ test_that(
         #   Use the existing object's colData to define coords before and after
         #   aligning to the nearest new array coordinates (as well as define the
         #   distance between spot centroids in pixels)
-        coords_new = colData(spe) |>
+        coords_new <- colData(spe) |>
             cbind(spatialCoords(spe)) |>
             as_tibble() |>
             select(
@@ -27,14 +27,14 @@ test_that(
             arrange(array_row) |>
             slice_head(n = 10) |>
             ungroup()
-        coords = coords_new |>
+        coords <- coords_new |>
             select(-c(array_row, array_col)) |>
             rename(
                 array_row = array_row_original, array_col = array_col_original
             )
-        inter_spot_dist_px = 277.1524
+        inter_spot_dist_px <- 277.1524
 
-        coords_err = .add_error_metrics(coords, coords_new, inter_spot_dist_px)
+        coords_err <- .add_error_metrics(coords, coords_new, inter_spot_dist_px)
 
         #   Two error-metric columns should be added
         expect_equal(
@@ -50,7 +50,7 @@ test_that(
 
         #   Shared neighbors must similarly be between 0 and 1 (it's a
         #   proportion). NAs are allowed when there are no neighbors originally
-        temp = coords_err$shared_neighbors[!is.na(coords_err$shared_neighbors)]
+        temp <- coords_err$shared_neighbors[!is.na(coords_err$shared_neighbors)]
         expect_equal(all((temp >= 0) & (temp <= 1)), TRUE)
     }
 )
diff --git a/tests/testthat/test-merge_overlapping.R b/tests/testthat/test-merge_overlapping.R
@@ -6,51 +6,51 @@ test_that(
         }
 
         #   Group colData by group and array coordinates
-        grouped_coldata = colData(spe) |>
+        grouped_coldata <- colData(spe) |>
             as_tibble() |>
             group_by(group, array_row, array_col)
 
         #   Find the first 100 keys that overlap other spots and don't, respectively
-        overlapping_keys = grouped_coldata |>
+        overlapping_keys <- grouped_coldata |>
             filter(n() > 1) |>
             slice_head(n = 2) |>
             ungroup() |>
             slice_head(n = 100) |>
             pull(key)
-        nonoverlapping_keys = grouped_coldata |>
+        nonoverlapping_keys <- grouped_coldata |>
             filter(n() == 1) |>
             ungroup() |>
             slice_head(n = 100) |>
             pull(key)
 
         #   Built a small SPE containing some overlaps and some non-overlapping spots
-        small_spe = spe[, c(overlapping_keys, nonoverlapping_keys)]
-        assays(small_spe) = list(counts = assays(small_spe)$counts)
-        reducedDims(small_spe) = list()
+        small_spe <- spe[, c(overlapping_keys, nonoverlapping_keys)]
+        assays(small_spe) <- list(counts = assays(small_spe)$counts)
+        reducedDims(small_spe) <- list()
 
         #   Merge overlapping spots
-        small_spe_merged = merge_overlapping(small_spe)
+        small_spe_merged <- merge_overlapping(small_spe)
 
         #   All array coordinates should have just one unique spot after merging
-        spots_per_coord = colData(small_spe_merged) |>
+        spots_per_coord <- colData(small_spe_merged) |>
             as_tibble() |>
             group_by(group, array_row, array_col) |>
             summarize(n = n()) |>
             pull(n)
         expect_equal(all(spots_per_coord == 1), TRUE)
 
         #   Grab a couple keys that overlap from different capture areas
-        overlapping_keys = colData(small_spe) |>
+        overlapping_keys <- colData(small_spe) |>
             as_tibble() |>
             group_by(group, array_row, array_col) |>
             filter(n() == 2, length(unique(capture_area)) == 2) |>
             slice_head(n = 2) |>
             ungroup() |>
             slice_head(n = 2) |>
             pull(key)
-        
-        #   The key 
-        dominant_key = overlapping_keys[
+
+        #   The key
+        dominant_key <- overlapping_keys[
             small_spe$exclude_overlapping[
                 match(overlapping_keys, small_spe$key)
             ]

diff --git a/vignettes/misc.Rmd b/vignettes/misc.Rmd
@@ -157,5 +157,5 @@ where `exclude_overlapping` is `FALSE`. Note that the function can be quite
 memory-intensive and time-consuming.
 
 ```{r "merge_overlapping", eval = FALSE}
-spe_merged = merge_overlapping(spe)
+spe_merged <- merge_overlapping(spe)
 ```
-Original file line number
+Diff line change
@@ Expand Up / @@ -83,7 +83,7 @@ rescale_fiji_inputs <- function(sample_info, out_dir) { @@
                 )
             )
         }
         if (!dir.exists(out_dir)) {
             stop("'out_dir' does not exist; please create it.")
         }
@@ Expand Down @@