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Appendix 1 – R-code
#Normalization
library(gcrma)
#in the following line, replace CEL directory and CDF name with respective folder and name of your custom CDF
set <- ReadAffy(celfile.path=<CEL directory>, cdfname = <custom CDF name>)
nset <- gcrma(set)
#Differential expression
library(limma)
design <- model.matrix(~ 0+factor(c(1,1,2,2)))
# exemplary design matrix for the case that samples 1 and 2 are control, 3 and 4 are treatment; adjust accordingly
fit <- lmFit(nset, design)
fit <- eBayes(fit)
topTable(fit)
#Induced network modules
library(BioNet)
#in the following line, replace PPI file with the file name of the PPI in standard interaction format (.sif)
ppi <- read.csv(<PPI file in sif>, sep="\t", header=T)
ppiG <- ftM2graphNEL(ppi, edgemode="undirected")
#in the following line, replace diffEx file with the table acuired in Method 3.1.2 step 5
deData <- read.csv(<diffEx file>, sep="\t", header=T)
subnet <- subNetwork(deData$Gene.ID, ppiG)
fb <- fitBumModel(pval, plot = FALSE)
#pval = 0.05, for example
scores <- scoreNodes(subnet, fb, fdr = 0.01)
#play around with the fdr value to acquire modules of interpretable size
module <- runFastHeinz(subnet, scores)
plotModule(module, scores = scores, diff.expr = logFC)