diff scran-normalize.R @ 0:9e2b0debeaea draft default tip

"planemo upload for repository https://github.com/ARTbio/tools-artbio/tree/master/tools/gsc_scran_normalize commit ddcf915dd9b690d7f3876e08b939adde36cbb8dd"

--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/scran-normalize.R	Thu Sep 26 10:50:32 2019 -0400
@@ -0,0 +1,91 @@
+# load packages that are provided in the conda env
+options( show.error.messages=F,
+       error = function () { cat( geterrmessage(), file=stderr() ); q( "no", 1, F ) } )
+loc <- Sys.setlocale("LC_MESSAGES", "en_US.UTF-8")
+warnings()
+
+library(optparse)
+library(scran)
+
+#Arguments
+option_list = list(
+  make_option(
+    c("-d", "--data"),
+    default = NA,
+    type = 'character',
+    help = "Input file that contains count values to transform"
+  ),
+  make_option(
+    c("-s", "--sep"),
+    default = '\t',
+    type = 'character',
+    help = "File separator [default : '%default' ]"
+  ),
+  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))
+
+if (opt$sep == "tab") {opt$sep = "\t"}
+
+data = read.table(
+  opt$data,
+  check.names = FALSE,
+  header = TRUE,
+  row.names = 1,
+  sep = opt$sep
+)
+
+## 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$method)
+
+  ## Compute sum factors
+  sce <- computeSumFactors(sce, cluster = clusters)
+} else {
+
+  ## Compute sum factors
+  sce <- computeSumFactors(sce)
+}
+
+sce <- normalize(sce)
+
+logcounts <- data.frame(genes = rownames(sce), round(logcounts(sce), digits=5), check.names = F)
+
+
+write.table(
+  logcounts,
+  opt$out,
+  col.names = T,
+  row.names = F,
+  quote = F,
+  sep = "\t"
+)