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I have been following the last DESeq2 pipeline to perform an RNAseq analysis. My problem is the rin of the experimental samples is quite low compared to the control ones. Iread a paper in which they perform RNAseq analysis with time-course RNA degradation and conclude that including RIN value as a covariate can mitigate some of the effects of low rin in samples.

My question is how I should construct the design in the DESeq2 object:

~conditions+rin
~conditions*rin
~conditions:rin

none of them... :)

I cannot find proper resources where explain how to construct these models (I am new to the field...) and I recognise I crashed against a wall with these kinds of things. I would appreciate also some links to good resources to be able to understand which one is correct and why.

Thank you so much

FrAoJm
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  • not really suitable for SO.. most like better if you post in https://bioinformatics.stackexchange.com/users or cross-validated. I can try to answer it for you – StupidWolf May 18 '20 at 16:44
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    I’m voting to close this question because it more appropriate for [Bioinformatics](https://bioinformatics.stackexchange.com/). – Ian Campbell May 18 '20 at 23:32

1 Answers1

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Turns out to be quite long for typing in a comment.

It depends on your data.

First of all, counts ~conditions:rin does not make sense in your case, because conditions is categorical. You cannot fit only an interaction term model.

I would go with counts ~condition + rin, this assumes there is a condition effect and a linear effect from rin. And the counts' dependency of rin is independent of condition.

As you mentioned, rin in one of the conditions is quite low, but is there any reason to suspect the relationship between rin and counts to differ in the two conditions? If you fit counts ~condition * rin, you are assuming a condition effect and a rin effect that is different in conditions. Meaning a different slope for rin effect if you plot counts vs rin. You need to take a few genes out, and see whether this is true. And also, for fitting this model, you need quite a lot of samples to estimate the effects accurately. See if both of these holds

StupidWolf
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