**20**wrote:

Hi,

I am trying to perform differential gene analysis using limma package along with qvalue package on several Affy microarrays. Basically I am comparing two conditions, let's say treated and untreated. I found that after running:

```
library(limma)
library(annaffy)
phenoData <- read.AnnotatedDataFrame("phenodata.txt")
data <- ReadAffy(celfile.path=".")
combinations <- factor(paste(pData(phenoData)[,1], sep = "_"))
design <- model.matrix(~combinations)
fit <- lmFit(eset, design)
fit <- eBayes(fit)
table <- topTable(fit,coef=2, 10000, adjust="fdr")
library(qvalue)
pvalues <- table$P.Value
hist(table$P.Value, nclass = 20)
qobj <- qvalue(p = table$P.Value, lambda=0)
summary(qobj)
pi0 <- qobj$pi0
qvalues <- qobj$qvalues
localFDR <- lfdr(p = table$P.Value, lambda = 0)
write.qvalue(qobj,file="qvalue.txt",sep="\t")
```

q-values I obtained from "qpackage" and p.adjusted values from "fdr" adjustment for multiple testing result in the same values for hundreds of genes making only 2 genes significant. I strongly believe that is not the actual case, as you can see great fold change for many of the genes. In addition, many p values fall below 0.05 indicating the significance of the genes. I was wondering if anyone could explain to me why this is the output I get and how should I overcome it to produce an accurate list of differentially expressed genes. Thank you so much.

Sincerely, Ula

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