[BioC] how edgeR control the outliers?

[Yuan Tian]

Dear all,

I use edgeR for differential expression analysis on a RNAseq dataset. But I found that edgeR is very sensitive to outlier samples. For example, for one gene, overall the expression pattern is similar between control group and experimental group, but there is one single sample which behaves very differently from the others, then this gene is very likely to be falsely detected as differentially expressed. So can anyone please tell me if there's any option in the algorithm that can control the outlier impact?

I'm thinking to use median read count value instead of mean read count value to fit the NB distribution, and to estimate the dispersions. Just wondering if there's an option available in edgeR? Or is there any other RNAseq DE analysis package which is less sensitive to outliers?

The outlier sample might be different when you look at different genes, so we can't take the whole sample out in the analysis. 

Yuan