Hello,

I am currently using DESeq2 to run some test on a case/control RNA-seq dataset with 2 time-point measurements (i.e. each sample has 2 measurements and is either classified as case or control). Cases and controls are not matched/paired.

I have read the tutorial and any other relevant info I could find but I can’t quite understand what is the role of the PatientID in the design formula. Most importantly I can’t decide whether I do need to add this in my design when I am looking for differentially expressed genes between cases and controls or between the 2 time points. Is this PatientID term essential in the formula as a flag for the presence of the same sample more than once (different time point) or does this term correct for the inter-individual variability in the model? I would really appreciate your help on this.

My question is more general but I’m also including a snapshot of the colData(dds) for your inconvenience:

One way to think about this is in the context of a paired t-test. In a 'regular' t-test you compute the mean of each group and then compare the means to see if you think there is an underlying population difference between the two groups. But if the two groups are somehow related (say you measured people's weights before and after they took a diet pill or placebo), then it would be more accurate to use a paired t-test, where you compute the difference in weight for each subject before and after the treatment, and then test to see if there is a consistent difference.

This 'adjusts out' any subject-specific weights, and allows you to test for the thing you really care about (e.g., the change in weight after taking the diet pill or placebo). Algebraically, when you put a patient-level factor in a model, you are doing essentially the same thing; you 'adjust out' the patient-specific gene expression and are then left with just the difference in gene expression between the two visits (or whatever), which is what you really care about.