I'm having difficulty conceptualizing how significance of logFC is calculated with `exactTest()` and `Benjamini-Hochberg` adjusted FDR calculation with `topTags()`.
Let's say you had `100 genes` with `5 control replicates` and `5 experimental replicates`, how are p-values for the logFC calculated and then how are they adjusted using the `Benjamini-Hochberg` method? For example consider `gene_1`, what if the control samples for `gene_1` were: `u = [5, 6, 5, 7, 5]` and the experimental replicate samples for `gene_1` were: `v = [9, 10, 11, 10, 8]`. Does the algorithm just do a t-test or wilcoxon between `log2(u)` and `log2(v)` to get the p-values? If so, wouldn't you need quite a bit of samples to get a reliable p-value?
Is this how the p-value is calculated? If so, how the FDR calculated from this? Sorry for all the questions, I'm just having trouble visualizing it. I just ran `edgeR` on a dataset with 3 control replicates and 3 experimental replicates. I'm not sure how the p-values and the FDR values are calculated and I'm trying to avoid blackboxing (i.e. using algos I don't understand conceptually).
Thanks for that! So does it use a negative binomial distribution of the controls and then find the likelihood of the experimentalsnto fit that model?