I was trying to do background correction using RMA for my GEO dataset, since the dataset was produced using Affymetrix array (GPL570). And preprocessing using limma package in R.
I got this tutorial for normalization From Data import to Normalization in Microarray Analysis using in R (Part I)
But unable to know, how to process this normalized output and give it as an input to identify the differentially expressed genes using limma. I have written the following code, but it returns 0 deg (topTable)
library("limma") library("affy") library("gcrma") setwd('E://GSE...7/') d1<-ReadAffy() data.rma <- expresso(d1,bgcorrect.method="rma",normalize.method="quantiles",pmcorrect.method="pmonly",summary.method="medianpolish") eset <- exprs(data.rma) sample <- factor(rep(c("Case","Cont"), each = 10)) design.mat <- model.matrix(~0+sample) colnames(design.mat) <- levels(sample) design.mat fit <- lmFit(eset,design.mat) fit3 <- eBayes(fit) deg <- topTable(fit3, coef = 2, p.value = 0.05, adjust.method = 'BH', number = nrow(eset), lfc > 2)
When I remove lfc criteria, I get genes with very less fold change value, none are above 2. On the contrary, when I perform mas5 background correction I get high fold change values.. How do I get the actual fold change values?