I have RNA-seq time course data consisting of 11 individual time points. I however do not have replicates for each time point. I am trying to fit a simple linear model of the form to detect oscillations:
time <- seq(2,22,by=2)
in.phase <- cos(2*pi/22*time)
out.phase <- sin(2*pi/22*time)
design <- model.matrix(~in.phase + out.phase)
My question is can my large residual degrees of freedom compensate for my lack of biological replicates at each time point. In other words, can I use the standard pipeline with estimateDisp(y, design, robust=TRUE) to process my data or do I need to (a) choose a reasonable BCV value (as suggested in the manual) (b) only estimate trended dispersion?
Following the standard pipeline, I was wondering if the oscillating genes (are obviously also the ones with lot of sample to sample variability in my case) get assigned larger than "reasonable" tag wise dispersion? I do not have problems with identifying them with the standard pipeline, but I am trying to understand what assumptions I am making.