Many thanks for your response, this is helpful. Really grateful
Feel free to split this into a different query ticket as I am not sure.
Regarding the differential and relative abundance, I have seen some papers run kruskal wallis test (more than 2 groups) or Mann Whitney (2 groups) on the relative abundance data results and consider this as differential abundance while others use ANCOM for differential abundance. Do you think there is any difference and can we argue that both could be called differential abundance?
Cumulative-Sum Scaling (CSS) implemented in metagenomeSeq, Median (MED) in DESeq2, Upper Quartile (UQ) and Trimmed Mean of M-values (TMM) in edgeR and Wrench, and Total-Sum Scaling (TSS) (relative abundance). ... “ELib-UQ” (Effective library size using UQ) and “ELib-TMM” (Effective library size using TMM)
ANCOM, ANCOM-BC, LEfSe, gneiss18, phylofactorization61,62, PhILR63, and selbal64
According to this paper they mentioned running Kruskal–Wallis test on the rarefied samples for differential abundance. Have you heard or have you seen it run on unrarefied samples?
I think the point of this paper and much of the recent literature is that it is not appropriate for the data. It ignores some basic assumptions around the distribution and structure.
Are there situations in which you might structure your data in such a way that it could be passed into a test that assumes normality? Absolutely, tools like ANCOM, Aldex2, Gneiss, and Phylofactor are all built on those types of transforms.
There are certainly other papers that evaluate KW or other techniques on other transforms. I dont have the exhaustive literature of how everyone has compared all their methods.
I will, however, mention that I'm putting together slides for a class discussion about this topic, and I'll share my first slide: