I'm relatively new to differential expression analysis of RNA-Seq data, and I'm working my way through the DESeq2 vignette to come to terms with how it works. I'm looking to obtain a set of genes with a log2 fold change >2, and an adjusted p-value of <0.001.
Following the "standard" procedure, I'm able to get the results using the following command:
res_groupA_vs_groupB <- results(dds,contrast=c("Tissue","groupA","groupB"),lfcThreshold=2,alpha=0.001)
This gave me sensible results, including adjusted p-values that I'm comfortable interpreting. I then noticed the lfcShrink function, and read about its benefits. I ran the following command:
res2_groupA_vs_groupB <- lfcShrink(dds,coef=2,type="apeglm",lfcThreshold=2)
In this case, s-values are provided, and from the documentation I can see these "provide the probability of false signs among the tests with equal or smaller s-value than a given given's s-value". I read through the Stephens (2016) reference to try to understand these more, but I'm still a little uncertain as to the interpretation of s-values. As stated above, I've decided a priori to focus on those genes with an adjusted p-value of <0.001, but I'm uncertain whether this same logic can be applied to s-values (i.e., focusing on genes with s-value <0.001). Can the interpretation be analogous, or am I on the wrong track here?
Any advice or suggestions for further reading would be greatly appreciated.