**70**wrote:

Hello, I am trying to analyze affymetrix microarray data (GSE27157). I did differential gene expression analysis using limma and used topTable() function to extract the top genes. I wanted to extract the genes with adj.p.value < 0.05, however filtering with this criteria returns nothing. The smallest amount for adj.p.value is 0.4 !!. Is it normal or there is a problem in my analysis? I did RMA to normalize my data and also tried with GCRMA but both returned the same result. Could I extract based on p.value instead of adj.p.value, in this case (p.value < 0.05 and lfc = 2) it returns 111 genes.

```
design <- model.matrix(~0 + samples)
colnames(design) <- c("Mut", "Wt")
fit <- lmFit(eset2, design)
contrast.matrix <- makeContrasts(Mut-Wt, levels = design)
fit.con <- contrasts.fit(fit, contrast.matrix)
fit.eb <- eBayes(fit.con)
dge <- topTable(fit.eb, number = 1000, lfc = 2)
dge[which(dge$P.Value < 0.05),]
dim(dge[which(dge$P.Value < 0.05),])
```

any help would be great appreciated!!

Hello, I am using limfit model for gene expression data but i am confused about the result. This is the first time i am using this, could anyone please help me about interpreting the results? Which part of the result should i include in my report and how to explain that part? Thanks in advance

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