# Why do I obtain very strange results with DESeq2?

I am using DESeq2 to perform a differential expression analysis, but I obtained very strange results for some genes. For some genes, I have very high log2FoldChange with very low p-value as displayed in the following table.

When I look the normalized expression values for these genes. I can see that these genes are expressed in only one single sample (condition1= samples from 1 to 7, condition2 = samples 8 to 13).

Could you please tell me how to avoid DESeq2 to detect these genes as differentially expressed ?

I am using to following commands:

dds                      <- DESeqDataSetFromHTSeqCount(sampleTable = sampleTable, directory = directory, design= ~ condition)
dds                      <- DESeq(dds)
res                      <- results(dds, contrast=c("condition",condition1,condition2))

• What are the size factors for these samples (sizeFactors(dds))? I suspect that sample10 is just really wonky and throwing everything off, so you'll probably want to just exclude it. Nov 7 '17 at 11:17
• I have check that but the factor is in the range of other samples... I have similar situations with other samples..
– Bob
Nov 7 '17 at 12:48
• Have you made any of the diagnostic plots related to cook's cutoff distance? If so, post some. If not, make some (see the here for an example). Nov 7 '17 at 12:52