Is more than two conditions possible in DESEQ?

[greener]

Going through the literature , it doesn't seem that you can provide more than two conditions for the negative binomial distribution. If someone knows a way to provide more than one I'd be very interested. Thanks
-Rich

[townway]

I have not made it either...hope some one can provide answers

[Simon Anders]

I've been asked about "more than two questions" several times and I still don't quite get what this is supposed to mean.

Of course you can compare as many conditions as you like with DESeq -- you just have to do so pair-wise.

If you have two conditions A and B, you may ask: Is the expression of my favorite gene larger in A or in B? How would you phrase such a "larger than" question with more than two conditions?

In the context of linear models, it is also common to ask: Given several treatments, does any of them have a significant effect, i.e., you test against the null hypothesis: None of the treatments have an effect. (Then, you may get a significant result, if you have evidence against the null even though the signal is not strong enough to tell you which condition is the one out.) This can be done with DESeq, too, with the GLM functionality, but I haven't seen many actual use cases yet where this contrast made sense.

[palmy]

Of course you can compare as many conditions as you like with DESeq -- you just have to do so pair-wise.

If you have two conditions A and B, you may ask: Is the expression of my favorite gene larger in A or in B? How would you phrase such a "larger than" question with more than two conditions?

I’m not sure but I think the experiment, which I currently try to analyse, deals with such a “larger than” question.

The experiment is about analysing circadian or rhythmic expression of genes at different timepoints of the day. Generally in such experiments it is not clear which conditions you have to compare pair wise. As there is no condition which can be seen as untreated or treated.
E.g. you have RNA samples of 4 different timepoints of a healthy patient and 4 RNA samples of the same timepoints of a patient with a certain health condition. If you now ask which genes were differentially expressed within the 4 different timepoints in the healthy patient but not in the sick patient, I think you have to compare all 4 conditions of the healthy patient at the same time.

I initially thought I could solve the problem; when I do pairwise comparisons between the different timepoints (e.g. timepoint A with B, timepoint B with C, timepoint C with D and timepoint D with A) but I think one get many false positives with such an analysis because of the multiple testing problem.

Could DESeq cope with such a problem?

[Simon Anders]

First of all: I don't think the multiple testing problem has anything to do with all this. Why would the usual Benjamini-Hochberg procedure not be applicable when you have a time course?

What you are missing is a null hypothesis. Once you have formulated it I can tell you whether DESeq can test it.

As a preliminary though exercise to get there, try to become clear what you mean, e.g., by "genes [that] were differentially expressed within the 4 different timepoints". No gene will keep its expression level perfectly constant over a time course. If a gene changes, this does not at all mean that it is under circadian control. To demonstrate this, you will need a couple of time points, let's say 8 per patient, taken, say, at midnight and a noon, and then you look for genes which are consistently higher (or lower) at daytime than at night. You test against the null hypothesis that the observed variation is only due to noise and not connected to the sleep-wake cycle. (Actually, this is a bad example, as you may have a hard time finding any such genes. After all, our whole metabolism and hence most biochemical processes in the cell are somehow coupled to this rhythm.)

Again: For any kind of statistical test (not just for DESeq), you have to choose a test statistic. In my example it might be "average expression at daytime" and "average expression by night", and the null hypothesis would be that they are the same and their ratio hence deviates from 1 only due to noise.

[Gordon Smyth]

Negative binomial modeling for multifactor RNA-Seq experiments with the edgeR package

The edgeR package on Bioconductor provides negative binomial modelling, in including multigroup tests, for arbitrarily complex RNA-Seq experiments. See the package vignette for more details, or post questions to the Bioconductor mailing list.