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Imagine I have a dataset of highly periodic gene expression, taken at densely spaced timepoints. I know that the expression is periodic, but different genes have different offsets.

It is trivial to convert my timestamps into a cosine (cos_time = cos(offset + time*pi*2/PERIOD)), so that this new variable is linear with the genes' expressions -- but only with the ones that have a peak at offset.

What if I could have multiple cos_times (let's call them phases) and then ask DESeq2: "OK, which gene's behavior is most linear with which phase?" Barring comparing p-values of each LRT test, is there a better way?

Disclaimer: I know about cosinor, Lomb-Scargle periodograms, ARS, JTK etc, and I know they are likely more applicable in this particular case. I'm interested in whether it is in general possible to ask such a question from DESeq2, even if the variables in question are not periodic. Phase just seemed like the simplest concept to demonstrate this on.

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If you have densely spaced time points the using non-linear regression to directly fit the signal to a cosine or sine curve, which would directly produce the offset, would be much simpler. DESeq2 wouldn't be the appropriate tool then, you'd use base R and the nls() function.

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If I were to do this, my first guess would be to add in the phases as a separate factor in the statistical model, something like:

~ Condition + sex + phase1 + phase2 + phase3 + ...
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  • $\begingroup$ And then run LRTs removing phases from the model one at a time? This will leave me with p-values from individual tests, and comparing p-values to each other is probably going to put the wrath of statisticians upon me... Or is there a way to run DESeq2 in such a way that it spits out the "best phase" for each row? $\endgroup$
    – Unknown artist
    Jun 17, 2018 at 22:27

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