DEXSeq for multifactor design
I am using DEXSeq for testing differential exon usage between two conditions: control and treatment. For each condition, I have 8 biological replicates (C1C8, and T1T8). The design is listed below.
condition subject C1 control 1 C2 control 2 C3 control 3 C4 control 4 C5 control 5 C6 control 6 C7 control 7 C8 control 8 T1 treatment 1 T2 treatment 2 T3 treatment 3 T4 treatment 4 T5 treatment 5 T6 treatment 6 T7 treatment 7 T8 treatment 8 As you can see from the last column, we have 8 subjects involved in the experiment. Subject 1 has both the control and the treatment, and so on for all the other subjects. This is different from the situation discussed in the DEXSeq vignette here, for example: design(pasillaExons) gives: condition type treated1fb treated singleread treated2fb treated pairedend treated3fb treated pairedend untreated1fb untreated singleread untreated2fb untreated singleread untreated3fb untreated pairedend untreated4fb untreated pairedend I think in the pasilla example, the biological replicates are all different. Thus in my situation, in order to see if there is differential exon usage between the treatment and control, can I do: (1) ignore the fact that each subject had both control and treatment? In this case, in my implementation, shall I write: f_dispersion = count ~ sample + condition * exon pExons = estimateDispersions(pExons, formula=f_dispersion) pExons = fitDispersionFunction(pExons) Null model: f_0 = count ~ sample + condition Alternative model: f_1 = count ~ sample + condition * I(exon == exonID) pExons = testForDEU(pExons, formula0 = f_0, formula1 = f_1) (2) incorporate the subject as a corvariate (coded that column as a factor), and then analyze in the GLM framework? In this case, in my implementation, shall I write: f_dispersion = count ~ sample + (condition + subject) * exon Null model: f_0 = count ~ sample + subject * exon + condition Alternative model: f_1 = count ~ sample + subject * exon + condition * I(exon == exonID) (3) I am not sure if including subject as a corvariate is the best approach in my situation. Are there any other options that I can consider? (4) I write the formula for null and alternative models exactly according to the vignette, but I am not sure if they are what I should put in R implementation. Thank you so much ;) 
You'll want option (2). This happened to be recently discussed on the bioconductor email list, so have a look at that thread.

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That's a really relevant post, and it's convenient to include the subject effect in the GLM setting ;) Can I know if, according to my design matrix above, the following formula are correct? f_dispersion = count ~ sample + (condition + subject) * exon Null model: f_0 = count ~ sample + subject * exon + condition Alternative model: f_1 = count ~ sample + subject * exon + condition * I(exon == exonID) Thanks! 
By my understanding, yes. Hopefully someone else will jump in if my understanding is wrong!

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According to this post (pretty recent!): the formula I wrote should be correct for the dispersion and testDEU ;) Thanks! 
Confirmation is always good :)

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