exDesign: numeric vs categorical variables and shrinkage method
- DESeq2 treats numeric experimental variables as numeric variables, not as discrete variables
- hence: all discrete variables should have values that are not numbers, e.g. litter1, litter, litter3 instead of 1, 2, 3
- DESeq2: one should go through these steps:
- contrast_oe <- c("sampletype", "MOV10_overexpression", "control")
- res_tableOE_unshrunken <- results(dds, contrast=contrast_oe, alpha = 0.05)
- res_tableOE <- lfcShrink(dds, contrast=contrast_oe, res=res_tableOE_unshrunken) This allows to use other approaches for shrinking the logFC than the DEQeq standard approach See:
What you observe is consistent with what we see in testing on the benchmarking data and on simulation data. If you just compare method="normal" to method="apeglm" or "ashr", the differences you are likely to see is that normal will shrink large effects even if they have high precision (so shrinking too much) and allow small effects to float around 0, while apeglm/ashr will not shrink the precise, large effects much at all and > the small effects which are indistinguishable from 0 will be shrunk to 0. Papers show that these other two approaches are more effective.