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# initial workspace setup
library(WGCNA);
stringsAsFactors = FALSE
# load expression data **assumes csv format row(traits)(columns info+samples)
wgcnaRawData <- read.csv(file = "wgcna_data.csv")
# transform expressionData
dataExpr <- as.data.frame(t(wgcnaRawData));
# data cleaning
# adopted from docs
gsg = goodSamplesGenes(dataExpr, verbose = 3);
if (!gsg$allOK)
{
# Optionally, print the gene and sample names that were removed:
if (sum(!gsg$goodGenes)>0)
printFlush(paste("Removing genes:", paste(names(datExpr0)[!gsg$goodGenes], collapse = ", ")));
if (sum(!gsg$goodSamples)>0)
printFlush(paste("Removing samples:", paste(rownames(datExpr0)[!gsg$goodSamples], collapse = ", ")));
# Remove the offending genes and samples from the data:
dataExpr <- dataExpr[gsg$goodSamples, gsg$goodGenes]
}
# network constructions and modules
# choose softthreshhold (Calculate soft threshold if the user specified the)
powers <- c(c(1:10), seq(from = 12, to=20, by=2))
sft <- pickSoftThreshold(dataExpr, powerVector = powers, verbose = 5)
# pass user options
network <- blockwiseModules(dataExpr,
#similarity matrix options
corType = "pearson",
#adjacency matrix options
power = sft$powerEstimate,
networkType = "unsigned",
#TOM options
TOMtype = "unsigned",
#module indentification
minmodulesSize = 30,
deepSplit = 5,
PamRespectsDendro = FALSE
)
# plot dendro add color
# Convert labels to colors for plotting
mergedColors = labels2colors(net$colors)
# Plot the dendrogram and the module colors underneath
# generate random name for png && save the image location
png("WGCNAoutput.png",width=1000,height=600,type='cairo-png')
plotDendroAndColors(network$dendrograms[[1]],mergedColors[net$blockGenes[[1]]],
"Module colors",
dendroLabels = FALSE, hang = 0.03,
addGuide = TRUE, guideHang = 0.05)
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