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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
results <- fromJSON(file = "file_path.json")
# 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))))
# 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)
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$goodGenes]
}
## network constructions and modules
# Allow multi-threading within WGCNA
enableWGCNAThreads()
# choose softthreshhold (Calculate soft threshold)
# xtodo allow users to pass args
powers <- c(c(1:10), seq(from = 12, to=20, by=2))
sft <- pickSoftThreshold(dataExpr, powerVector = powers, verbose = 5)
# pass user options
network <- blockwiseModules(dataExpr,
#similarity matrix options
corType = corType,
#adjacency matrix options
power = 5,
networkType = "unsigned",
#TOM options
TOMtype = TOMtype,
#module indentification
minmodulesSize = minModuleSize,
deepSplit = 3,
PamRespectsDendro = FALSE
)
genImageRandStr <- function(prefix){
randStr <- paste(prefix,stri_rand_strings(1, 9, pattern = "[A-Za-z0-9]"),sep="_")
return(paste(randStr,".png",sep=""))
}
mergedColors <- labels2colors(network$colors)
imageLoc <- file.path(imgDir,genImageRandStr("WGCNAoutput"))
imageLoc
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)
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