# generate graphs for estimating soft thresholding parameters
# inputs:
# CEL files
### dependencies
library(optparse)
library(stringr)
library(oligo)
library(Biobase)
library(genefilter)
library(WGCNA)
library(flashClust)
library(hugene10sttranscriptcluster.db)
library(genefilter)
options(stringsAsFactors = FALSE)
allowWGCNAThreads()
source("../R/eset_tools.R")
### command line options
option_list <- list(
make_option("--cel_path", default="../data/TransplantCELs/batch1_2009",
help="Top level directory to start recursive search for *.CEL files."),
make_option("--eset", default = NULL, help = "ExpressionSet object saved as an RData file (must be saved as variable named eset)."),
make_option("--out_dir", default="../results",
help="Location to save the ExpressionSet object. Defaults to ./eset.Rdata"),
make_option("--verbose", action="store_true", default=FALSE,
help="Print extra output advising the user of progression through the analysis.")
)
args <- parse_args(OptionParser(option_list=option_list))
if (is.null(args$eset)){
write("No ExpressionSet RData found - calculating expression set from CEL files.", stderr())
cel_files = list.files(path = args$cel_path, pattern = ".*CEL", all.files = FALSE, full.names = TRUE, recursive = TRUE, ignore.case = TRUE)
eset = rma_eset(CEL_list = cel_files)
if (!file.exists("../data/HuGene-1_0-st-v1.na35.hg19.transcript.csv")){
write("Did not find HuGene probe annotation file - download from
http://www.affymetrix.com/support/technical/annotationfilesmain.affx and
put in the data subdirectory", stderr())}
affy_annotation = "../data/HuGene-1_0-st-v1.na35.hg19.transcript.csv"
# filter for genes/probes with entrez ID - this will have the same set of genes as eset23
eset <- gene_filter_eset(eset, affy_annotation)
} else {
load(args$eset) # should load an ExpressionSet object
}
colnames(eset) <- str_extract(colnames(eset), "[6][0-9][0-9]")
expr_data = t(exprs(eset))
### generate network using different soft thresholding parameters
powers = c(c(1:10), seq(from = 12, to=20, by=2))
# Call the network topology analysis function
sft = pickSoftThreshold(expr_data, powerVector = powers, verbose = 5)
write(sprintf("Writing output into %s", args$out_dir), stderr())
dir.create(args$out_dir)
pdf(paste0(args$out_dir, "/soft_thresholding_correlation.pdf"))
# from tutorial
par(mfrow = c(1,2))
cex1 = 0.9
# Scale-free topology fit index as a function of the soft-thresholding power
plot(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
xlab="Soft Threshold (power)",ylab="Scale Free Topology Model Fit,signed R^2",type="n",
main = paste("Scale independence"));
text(sft$fitIndices[,1], -sign(sft$fitIndices[,3])*sft$fitIndices[,2],
labels=powers,cex=cex1,col="red");
# this line corresponds to using an R^2 cut-off of h
abline(h=0.90,col="red")
# Mean connectivity as a function of the soft-thresholding power
plot(sft$fitIndices[,1], sft$fitIndices[,5],
xlab="Soft Threshold (power)",ylab="Mean Connectivity", type="n",
main = paste("Mean connectivity"))
text(sft$fitIndices[,1], sft$fitIndices[,5], labels=powers, cex=cex1,col="red")
dev.off()
write("The plot produced can be used to pick a soft thresholding parameter
for subsequent analyses. Red line is drawn at 0.90, but k should be
chosen at the point where correlation begins to level out and where
mean connectivity is at a reasonable value.", stderr())
write("Saving expression set in out dir.", stderr())
save(eset, file = paste0(args$out_dir, "/expression_set.RData"))
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