I did differential expression analysis on different datasets, and I noticed some unwilling outcome. E.g. in "GSE32280", the author says there are 10s - hundresd of DEGs between MDD and Health Control (used SVM not R package) to classify DEGs. And I used Limma package which came out 0 DEG.
Can someone provide own experience what to tell about such outcome? and which one should we more depend on? SVM or Limma? FYI, smallest adjusted-P from Limma was around 0.4.
Reference (which was not added in GEO database by them yet): PMC3278427
Secondly, from a study, in case the author used a commercial software to come out with 100 DEGs and if I use Limma to come out with 1000 DEGs, should I trust my instinct and result more? :)
Thank you very much for all
Edit: Code added
library(limma) targets <- readTargets("phenotype.txt") data <- ReadAffy(filenames=targets$FileName) eset <- rma(data) design <- model.matrix(~ -1+factor(targets$Targets)) colnames(design) <- c("Disease","Control") contrast.matrix <- makeContrasts(Disease-Control, levels=design) fit <- lmFit(eset, design) fit2 <- contrasts.fit(fit, contrast.matrix) fit2 <- eBayes(fit2) results <- topTable(fit2, coef=1, adjust="BH", sort.by="B",lfc=0, p.value=0.05, number=1000)