DESeq2 calling DEGs despite detection in few replicates
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lnblock2 • 0
@326247d3
Last seen 3.1 years ago

Hi, this is my first time doing differential expression analysis and I have a few questions about the data output. My data was collected from 4 different cell lines derived from 4 different animals (as biological replicates). We are getting inconsistent calling of DEGs and I'm not sure why. Attached are a few scatter plots of the genes in question.

For (B) it was called DEG even though it was only detected in one sample. For (A & C) these were NOT identified as a DEG with DESeq2 but were identified with edgeR's glmQLfit().

For DESeq2 I ran this with the default parameters. Any suggestions on which parameters I could change to account for variation between replicates? Are outliers handled differently when 3 out of 4 samples are highly expressed compared to when only 1 out for 4 samples are highly expressed?

I can add the code if that would help but it's more of a question about the algorithm itself. I can also provide more examples if that would be helpful. Thank you!

NOT identified as a DEG with DESeq2 but was identified with edgeR's glmQLfit(). NOT identified as a DEG with DESeq2 but was identified with edgeR's glmQLfit(). Was called DEG even though it was only detected in one sample.

DESeq2 • 625 views
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swbarnes2 ★ 1.3k
@swbarnes2-14086
Last seen 23 hours ago
San Diego

The software is correctly detecting a difference in averages between the two groups. It's really not the software's place to be deciding if those expression patterns are artifacts or not. If you think they are artifacts, you should remove them yourself.

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