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-rw-r--r--scripts/wgcna_analysis.R23
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]]],