From 743cf253c9576bf543c2e17d11d0980abb85d6a0 Mon Sep 17 00:00:00 2001 From: Christophe Antoniewski Date: Thu, 7 Nov 2024 22:39:03 +0100 Subject: [PATCH] reindent R code --- tools/gsc_scran_normalize/scran-normalize.R | 109 ++++++++++---------- 1 file changed, 55 insertions(+), 54 deletions(-) diff --git a/tools/gsc_scran_normalize/scran-normalize.R b/tools/gsc_scran_normalize/scran-normalize.R index e81e97c1d..c6c41bc0c 100644 --- a/tools/gsc_scran_normalize/scran-normalize.R +++ b/tools/gsc_scran_normalize/scran-normalize.R @@ -1,8 +1,9 @@ -options(show.error.messages = FALSE, - error = function() { - cat(geterrmessage(), file = stderr()) - q("no", 1, FALSE) - } +options( + show.error.messages = FALSE, + error = function() { + cat(geterrmessage(), file = stderr()) + q("no", 1, FALSE) + } ) loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8") warnings() @@ -13,63 +14,63 @@ library(dynamicTreeCut) # Arguments option_list <- list( - make_option( - c("-d", "--data"), - default = NA, - type = "character", - help = "Input file that contains count values to transform" - ), - make_option( - "--cluster", - default = FALSE, - action = "store_true", - type = "logical", - help = "Whether to calculate the size factor per cluster or on all cell" - ), - make_option( - c("-m", "--method"), - default = "hclust", - type = "character", - help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]" - ), - make_option( - "--size", - default = 100, - type = "integer", - help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]" - ), - make_option( - c("-o", "--out"), - default = "res.tab", - type = "character", - help = "Output name [default : '%default' ]" - ) + make_option( + c("-d", "--data"), + default = NA, + type = "character", + help = "Input file that contains count values to transform" + ), + make_option( + "--cluster", + default = FALSE, + action = "store_true", + type = "logical", + help = "Whether to calculate the size factor per cluster or on all cell" + ), + make_option( + c("-m", "--method"), + default = "hclust", + type = "character", + help = "The clustering method to use for grouping cells into cluster : hclust or igraph [default : '%default' ]" + ), + make_option( + "--size", + default = 100, + type = "integer", + help = "Minimal number of cells in each cluster : hclust or igraph [default : '%default' ]" + ), + make_option( + c("-o", "--out"), + default = "res.tab", + type = "character", + help = "Output name [default : '%default' ]" + ) ) opt <- parse_args(OptionParser(option_list = option_list), - args = commandArgs(trailingOnly = TRUE)) + args = commandArgs(trailingOnly = TRUE) +) data <- read.table( - opt$data, - check.names = FALSE, - header = TRUE, - row.names = 1, - sep = "\t" + opt$data, + check.names = FALSE, + header = TRUE, + row.names = 1, + sep = "\t" ) ## Import data as a SingleCellExperiment object sce <- SingleCellExperiment(list(counts = as.matrix(data))) if (opt$cluster) { - clusters <- quickCluster(sce, min.size = opt$size, method = opt$method) + clusters <- quickCluster(sce, min.size = opt$size, method = opt$method) - ## Compute sum factors - sce <- computeSumFactors(sce, cluster = clusters) + ## Compute sum factors + sce <- computeSumFactors(sce, cluster = clusters) } else { - - ## Compute sum factors - sce <- computeSumFactors(sce) + ## Compute sum factors + sce <- computeSumFactors(sce) } sce <- logNormCounts(sce) @@ -78,10 +79,10 @@ logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits = 5) write.table( - logcounts, - opt$out, - col.names = TRUE, - row.names = FALSE, - quote = FALSE, - sep = "\t" + logcounts, + opt$out, + col.names = TRUE, + row.names = FALSE, + quote = FALSE, + sep = "\t" )