How to analyze RNAseq data with two/multiple treatment groups and each subject has pre and post-treatment measures

Question

[wwu222]

Hi,

I am working on a set of RNAseq data that has the following design:

1. patients are randomized to different treatment groups: control, trt1 and trt2
2. each patient has pre and post treatment RNAseq data: time0 , time1

I am interested in getting treatment effect (not time effect). The model mentioned in DESeq2, edgeR and Limma helps to get time effect which is not the interest.

Any suggestion how to get treatment effect for RNAseq data with this design?

Thank you very much!

Answer 1

[Kevin Blighe]

To get treatment effect across time, you can perform ANOVA (LRT - likelihood ratio test):

dds <- DESeqDataSetFromMatrix(..., design = ~ treatment + time + treatment:time)
dds <- DESeq(dds, test="LRT", reduced = ~ treatment + time)
res <- results(dds)

If you simply want to 'ignore' the time effect and just control for it, i.e., as a covariate, then consider simply doing ~ treatment + time, followed by pairwise comparisons between your treatment levels.

[wwu222]

Hi Kevin,

Thank you very much for your timely reply and great suggestions. I think the second suggestion is the answer I am looking for. However, each patient has pre, post paired design, I am wondering whether we have to consider this in the model?

Thank you very much!

[Kevin Blighe]

So, you want to adjust for the patient-specific effects, too? - then you can include PatientID as a covariate.

[wwu222]

Hi Kevin, really appreciate your suggestion! I will try it out. Thank you very much!

[Kevin Blighe]

Cool. The model may then simply be ~ PatientID + treatment. This model is then effectively a paired analysis and looking at the effect of treatment. Time is captured to some level via PatientID. These model formulae can be somewhat confusing and you should always get multiple opinions.