DESeq2 analysis with high within-group variability across several groups
1
0
Entering edit mode
fl ▴ 20
@fl-16173
Last seen 3 months ago
Germany

Hello,

I am carrying out a DESeq2 analysis of a dataset with 16 groups and 4 replicates per group. I applied the variance stabilizing transformation, removed batch effects using limma::removeBatchEffect and plotted the PCA results. The PCA plot shows high variability within groups: PCA plot

I ran a differential expression analysis with all samples together and identified up to 8 differentially expressed genes. However, when I split the data into pairs of groups (creating a separate DESeqDataSet object for each treatment), I found many more significant differences, with 6 groups having between 100 to 250 differentially expressed genes. I know that the DESeq2 vignette recommends to split the data into pairs of groups when a particular treatment has much higher within-group variability. What would be the recommended approach when most groups have high within-group variability?

I also performed other analyses using sleuth and limma, and it seems only limma::voomWithQualityWeights identifies more than 100 differentially expressed genes in some groups when running samples from all groups together.

Any help is greatly appreciated.

DESeq2 • 1.0k views
ADD COMMENT
0
Entering edit mode
@mikelove
Last seen 22 hours ago
United States

I don't have any particular advice here, but I'd warn that it seems like there aren't many differences and that by trying many options you may end up losing control of FPR simply by iterating across many combinations of methods.

ADD COMMENT

Login before adding your answer.

Traffic: 721 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6