Question: significant genes with DESeq2 plenty of outliers and zero counts
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22 months ago by
aec50
aec50 wrote:

Dear all,

I run DESeq2_1.18.1 with default options and obtained odd results. The significant transcripts (FDR<5%) are due to one or two outlier samples.

I checked the expression values and all samples except the outliers have 0 counts,

How can I deal with this situation? Which parameters should I modify? changing Cook's distance? disable independent filtering?

Thanks,

modified 22 months ago by Michael Love26k • written 22 months ago by aec50
Answer: significant genes with DESeq2 plenty of outliers and zero counts
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22 months ago by
Michael Love26k
United States
Michael Love26k wrote:

It could be you have too few samples for the outlier detection to kick in, or the counts are not high enough to count as outliers. Either way, you can perform more aggressive filtering to remove genes with a count in only one or two samples:

dds <- estimateSizeFactors(dds)
keep <- rowSums(counts(dds,normalized=TRUE) >= 10) >= 3
dds <- dds[keep,]

Hi Michael,

I have 20 samples, 10 per group ( I think DEseq2 requires minimum of 7 for outliers).

and the normalized counts of 2 significant genes:

s1 s2 s3 s4 s5 s6 s7 s8 s9 s10 s11 s12 s13 s14 s15 s16 s17 s18 s19 s20
transcript1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4706
transcript2 0 0 0 0 0 0 0 0 0 0 0 268.4 0 0 0 0 0 0 0 0

they appear to be high enough...

It's hard to say for a given gene why filtering/replacement does or does not apply, because it depends also on the dispersion of all the other genes. Nevertheless, the above code should help you here.