I have been running my microarray data using limma and I'd like to ask a question. What is the best way to reduce the number of differentially expressed genes? As you can see below, I have used the treat method to reduce it and no differentially expressed genes were listed in the Contrast(3). But, when I create a plot to represent of differential expression results ( Figure Contrast(3)), it is possible to identify significant genes. So, Why does it happen? Sorry for my basic question? Thanks in advance.
fit2 <- contrasts.fit(fit, cont.matrix) efit2 <- eBayes(fit2) summary(decideTests(fit2)) summary(decideTests(fit2)) Contrast(1) Contrast (2) Contrast(3) -1 2658 6566 942 0 56790 51236 59649 1 3528 5174 2385 tfit <- treat(fit2, lfc=1) dt <- decideTests(tfit) summary(dt) summary(dt) Contrast(1) Contrast(2) Contrast(3) -1 57 783 0 0 62672 61728 62976 1 247 465 0
plotMD(tfit, column=3, status=dt[,1],main=colnames(tfit), xlim=c(5,18))
I do not know how to insert A figure here.
So, let me try to explain the result from the figure. So, when looking at the figure (Log-fold change x Average log-Expression), I can see a few genes highlighted in green and red. These genes are between the range of -2<log-Fold change <2. My question is: If I got 0 up- and down-regulated genes in the contrast (3), why are these genes highlighted in the contrast(3) figure?