diff options
| -rw-r--r-- | scripts/wgcna_analysis.R | 23 |
1 files changed, 15 insertions, 8 deletions
diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R index d0ba91a..73d0e3f 100644 --- a/scripts/wgcna_analysis.R +++ b/scripts/wgcna_analysis.R @@ -13,12 +13,19 @@ imgDir = Sys.getenv("GENERATED_IMAGE_DIR") results <- fromJSON(file = "file_path.json") -# trait_sample_data <- results$trait_sample_data -trait_sample_data <- do.call(rbind, results$trait_sample_data) +# parse the json data input + +minModuleSize <-results$minModuleSize + +TOMtype <-results$TOMtype + +corType <-results$corType +# + +trait_sample_data <- do.call(rbind, results$trait_sample_data) dataExpr <- data.frame(apply(trait_sample_data, 2, function(x) as.numeric(as.character(x)))) -# trait_sample_data <- as.data.frame(t(results$trait_sample_data)) # transform expressionData dataExpr <- data.frame(t(dataExpr)) @@ -49,18 +56,18 @@ sft <- pickSoftThreshold(dataExpr, powerVector = powers, verbose = 5) # pass user options network <- blockwiseModules(dataExpr, #similarity matrix options - corType = "pearson", + corType = corType, #adjacency matrix options power = 5, networkType = "unsigned", #TOM options - TOMtype = "unsigned", + TOMtype = TOMtype, #module indentification - minmodulesSize = 30, - deepSplit = 5, + minmodulesSize = minModuleSize, + deepSplit = 3, PamRespectsDendro = FALSE ) @@ -76,7 +83,7 @@ genImageRandStr <- function(prefix){ mergedColors <- labels2colors(network$colors) imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput")) - +imageLoc png(imageLoc,width=1000,height=600,type='cairo-png') plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]], |