**130**wrote:

Hi all,

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)
```

Could you give the reference of the paper you are referring to. Also, SVM is a classification method not a DEG test. On a general note, one could expect that for DE studies, the desired outcome is normally DEGs so authors might tend to report the method that gives them DEGs, such studies are hard to write up if there are no or little DEGs.

48kSure. I had added that up. Its pubmed id: PMC3278427:

In the paper, they mentioned as Differentially Expressed Signature.

130