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| author | Alexander Kabui | 2021-09-23 12:22:10 +0300 |
|---|---|---|
| committer | Alexander Kabui | 2021-09-23 12:22:10 +0300 |
| commit | 3d36fe96c94cebb6e7ea93b735148b25c4b95f6d (patch) | |
| tree | c6f25bd191ae16e0637b3b9f5ad6823ef521c308 /scripts/wgcna_analysis.R | |
| parent | 0f871f49e749eb625f58326adf8f80b3d3b5b932 (diff) | |
| download | genenetwork3-3d36fe96c94cebb6e7ea93b735148b25c4b95f6d.tar.gz | |
load data from json file and and convert to dt
Diffstat (limited to 'scripts/wgcna_analysis.R')
| -rw-r--r-- | scripts/wgcna_analysis.R | 22 |
1 files changed, 12 insertions, 10 deletions
diff --git a/scripts/wgcna_analysis.R b/scripts/wgcna_analysis.R index 267cd86..d0ba91a 100644 --- a/scripts/wgcna_analysis.R +++ b/scripts/wgcna_analysis.R @@ -2,26 +2,29 @@ library(WGCNA); library(stringi); +library(rjson) options(stringsAsFactors = FALSE); imgDir = Sys.getenv("GENERATED_IMAGE_DIR") -# load expression data **assumes csv format row(traits)(columns info+samples) +# load expression data **assumes from json files row(traits)(columns info+samples) # pass the file_path as arg -inputData <- read.csv(file = "wgcna_data.csv") +results <- fromJSON(file = "file_path.json") -# transform expressionData +# trait_sample_data <- results$trait_sample_data +trait_sample_data <- do.call(rbind, results$trait_sample_data) -dataExpr <- as.data.frame(t(inputData)); -## data cleaning +dataExpr <- data.frame(apply(trait_sample_data, 2, function(x) as.numeric(as.character(x)))) +# trait_sample_data <- as.data.frame(t(results$trait_sample_data)) +# transform expressionData +dataExpr <- data.frame(t(dataExpr)) gsg = goodSamplesGenes(dataExpr, verbose = 3); # https://horvath.genetics.ucla.edu/html/CoexpressionNetwork/Rpackages/ - if (!gsg$allOK) { if (sum(!gsg$goodGenes)>0) @@ -49,7 +52,7 @@ network <- blockwiseModules(dataExpr, corType = "pearson", #adjacency matrix options - power = sft$powerEstimate, + power = 5, networkType = "unsigned", #TOM options TOMtype = "unsigned", @@ -70,14 +73,13 @@ genImageRandStr <- function(prefix){ return(paste(randStr,".png",sep="")) } -mergedColors <- labels2colors(net$colors) +mergedColors <- labels2colors(network$colors) imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput")) - png(imageLoc,width=1000,height=600,type='cairo-png') -plotDendroAndColors(network$dendrograms[[1]],mergedColors[net$blockGenes[[1]]], +plotDendroAndColors(network$dendrograms[[1]],mergedColors[network$blockGenes[[1]]], "Module colors", dendroLabels = FALSE, hang = 0.03, addGuide = TRUE, guideHang = 0.05) |