As statistics is not my strong suit, I had some questions involving analyses of our ribosomal profiling and RNA-seq data in a 2x2 experiment. (two strains, treated v untreated).
- In brief, our experiment is looking at the translational efficiency (TE) of the transcriptome when translation is disrupted. As such, we expect to see a global decrease in counts of most genes in our ribosomal protected (RP) reads. What kind of normalization would one use for these conditions? (perhaps checking the distribution of counts to ensure it follows a negative binomial?)
- In addition to normalization, would filtering be a potential option? Is there some sort of "good" baseCounts filter ? My guess is that more specific normalization might help this problem.
- Reading other posts, the test of the ratio of ratios is the interaction term for a two factor experiment. How would one apply this for a 2x2 experiment? (e.g. fold change in the TE of the interaction effect of treatment and strain). Is this just performing the same analyses for the interaction effect (~ strain + treatment + strain:treatment) except passing a matrix of FPKM-RP/FPKM-mRNA values to DESeq2?
Any insights or help would be much appreciated!