I have a very small shRNA-seq screen dataset that contains only 36 shRNAs (targeting a total of 17 genes) with one shRNA being a control (i.e. no change in its representation is expected over time). Most of the other shRNAs targeting genes that are thought to be essential for cell survival. It is a time course experiment with two replicates for each time points (4 time points) and we are interested to know if any of these shRNAs is consistently depleted over time.
I have read through the examples provided in http://bioinf.wehi.edu.au/shRNAseq/pooledScreenAnalysis.pdf and noticed that most of the examples do not apply any normalisation (with calcNormFactors) to account for compositional difference between the samples. So I was wondering if normalisation is not necessary for shRNA screen data ? And am I right to think that sequencing depth is automatically account for during the differential representation analysis step ?
I would appreciate if you could advise me on the best way to perform time course analysis with just 2 replicates on such a small shRNA-seq screen data ? And possibly the best way to normalise the data to correct for library size and compositional bias (if at all needed) ?
Thank you for your advice in advance