Merge branch 'main' of https://github.com/genenetwork/genenetwork3 into feature/add_rqtl_pairscan

1 files changed, 115 insertions, 0 deletions

diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R
new file mode 100644
index 0000000..17b3537
--- /dev/null
+++ b/scripts/wgcna_analysis.R
@@ -0,0 +1,115 @@
+
+
+library(WGCNA);
+library(stringi);
+library(rjson)
+
+options(stringsAsFactors = FALSE);
+
+imgDir = Sys.getenv("GENERATED_IMAGE_DIR")
+# load expression data **assumes from json files row(traits)(columns info+samples)
+# pass the file_path as arg
+# pass the file path to read json data
+
+args = commandArgs(trailingOnly=TRUE)
+
+if (length(args)==0) {
+  stop("Argument for the file location is required", call.=FALSE)
+} else {
+  # default output file
+  json_file_path  = args[1]
+}
+
+inputData <- fromJSON(file = json_file_path)
+
+
+trait_sample_data <- do.call(rbind, inputData$trait_sample_data)
+
+dataExpr <- data.frame(apply(trait_sample_data, 2, function(x) as.numeric(as.character(x))))
+# transform expressionData
+
+dataExpr <- data.frame(t(dataExpr))
+gsg = goodSamplesGenes(dataExpr, verbose = 3)
+
+if (!gsg$allOK)
+{
+if (sum(!gsg$goodGenes)>0)
+printFlush(paste("Removing genes:", paste(names(dataExpr)[!gsg$goodGenes], collapse = ", ")));
+if (sum(!gsg$goodSamples)>0)
+printFlush(paste("Removing samples:", paste(rownames(dataExpr)[!gsg$goodSamples], collapse = ", ")));
+# Remove the offending genes and samples from the data:
+dataExpr <- dataExpr[gsg$goodSamples, gsg$goodGenes]
+}
+
+## network constructions and modules
+
+names(dataExpr) = inputData$trait_names
+
+# Allow multi-threading within WGCNA
+enableWGCNAThreads()
+
+# choose softthreshhold (Calculate soft threshold)
+# xtodo allow users to pass args
+
+powers <- c(c(1:10), seq(from = 12, to=20, by=2)) 
+sft <- pickSoftThreshold(dataExpr, powerVector = powers, verbose = 5)
+
+# check the power estimate
+
+if (is.na(sft$powerEstimate)){
+  powerEst = 1
+}else{
+  powerEst = sft$powerEstimate
+}
+
+# pass user options
+network <- blockwiseModules(dataExpr,
+                  #similarity  matrix options
+                  corType = inputData$corType,
+                  #adjacency  matrix options
+
+                  power = powerEst,
+                  networkType = "unsigned",
+                  #TOM options
+                  TOMtype =  inputData$TOMtype,
+
+                  #module indentification
+                  verbose = 3,
+
+                  minmodulesSize = inputData$minModuleSize,
+                  deepSplit = 3,
+                  PamRespectsDendro = FALSE
+                )
+
+
+
+genImageRandStr <- function(prefix){
+
+	randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
+
+	return(paste(randStr,".png",sep=""))
+}
+
+mergedColors <- labels2colors(network$colors)
+
+imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput"))
+png(imageLoc,width=1000,height=600,type='cairo-png')
+
+plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]],
+"Module colors",
+dendroLabels = FALSE, hang = 0.03,
+addGuide = TRUE, guideHang = 0.05)
+
+
+json_data <- list(input = inputData,
+  output = list(ModEigens=network$MEs,
+   soft_threshold=sft$fitIndices,
+   blockGenes =network$blockGenes[[1]],
+   net_colors =network$colors,
+   power_used_for_analy=powerEst,
+   net_unmerged=network$unmergedColors,
+   imageLoc=imageLoc))
+
+json_data <- toJSON(json_data)
+
+write(json_data,file= json_file_path)
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