RNA-Seq analysis with DESeq2: showing two conditions do *not* significantly differ
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Ndimensional ▴ 20
Last seen 10 weeks ago
United States

Hello bioconductor,

I am working with a collaborator who is interested in showing that two states that people in the field might expect to differ in fact do not differ. I am writing to ask what are the most convincing ways to do this both visually and statistically.

Here are my thoughts:

Visually, I think if we took the gene signature that would be expected to differ most strongly, then we showed that those genes in fact do not differ using a heatmap and a distance matrix, that could be a good way to make the argument visually in a context that is relevant in the field.

Statistically, we might think about using permutation-based testing to show that the p-value distribution of the conditions does not significantly differ from random permutations of the labels. This option seems more rigorous numerically but I think it is also less likely to be immediately grasped by a larger audience.

What other approaches could be fruitful to take?

Thank you!

deseq2 RNA-Seq uniform distribution data visualization • 549 views
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Last seen 8 hours ago
United States

If you read over the DESeq2 2014 paper you'll see we created a method for this, and it's also discussed in the vignette. Search for altHypothesis.

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my man - that webpage is practically my backyard. Don't know how I missed this feature before. Thank you. I'll dive in there and into the documentation itself. Appreciate your great work. VL

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Last seen 7 months ago
United States

You could also set altHypothesis = "lessAbs" in your DESeq2::results() call to identify genes whose differential expression is below some threshold.

Check out the Tests of log2 fold change above or below a threshold in the DESeq2 vignette.

Edit: woops, looks like Michael beat me to the punch


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