#To use this script, basically copy and paste the code below in the R command line. #Please, remember to adjust the paths to point to the correct locations of your files. #You need to have the affy package installed. If you don't, type in the R command line: #source('http://bioconductor.org/biocLite.R') #biocLite('affy') setwd('~/NetDecoder_Example/raw_data/CEL_files') #path to your .CEL files source('../NetDecoder_utils.R') library(affy) prefix <- '../expDat' files <- list.files('.', pattern='CEL') raw.data <- ReadAffy(verbose = FALSE, filenames = files) data.rma.norm <- rma(raw.data) expRaw <- exprs(data.rma.norm) fname <- paste(prefix, 'raw.R', sep='_') save(expRaw, file=fname) #comment this line if you do not want to save RMA normalized data platform <- "hgu133plus2" #change this if your microarray data was profiled in another platform geneTab<-Norm_loadAnnotation(platform); rownames(geneTab)<-as.vector(geneTab$probe_id) ggs<-intersect(rownames(expRaw), as.vector(geneTab$probe_id)); expRaw2<-expRaw[ggs,]; gTab<-geneTab[ggs,]; expClean<-Norm_cleanExp(expRaw2, gTab, "symbol"); x <- sub('.cel', '', colnames(expClean), ignore.case=TRUE) colnames(expClean) <- gsub("-", "_", x) fname <- paste(prefix, 'clean.R', sep='_') save(expClean, file=fname) #expClean is a clean expression matrix with gene symbol annotations #this is the gene expression matrix that you have to provide to the script NetDecoder_Create_EWN.R #where EWN means Edge-Weighted Networks.