High rates of significant results using NEGBIN

Downstream analysis and statistics MaAsLin
1

Hi folks,

I am analyzing a metagenomic dataset with abundance calculated by kraken2. I can use either counts or proportions of reads. I have one metadata category at the moment with one reference category and 3 test categories. If I run this setup using read proportions through Maaslin2 with LM/TSS/LOG, I get no significant results. However, if I use raw read counts with NEGBIN/CSS/NONE, I get plenty of significant results that somewhat seem reasonable.
I read through the Maaslin2 manuscript and noticed that NEGBIN had a very high rate of false positive discovery (Fig. 2). Are the results I am seeing due to NEGBIN’s inflated FDR? Or are these results real?

2

It’s not possible to answer your question without data, but you’re right to be skeptical given the disagreement. I suggest you plot the raw data (both as proportions and counts) for some of the features with the biggest discrepancy and judge which model would be more appropriate.

3

Thanks for the idea!
Just to confirm, the negative binomial model shown in the Maaslin paper is the same method as NEGBIN in the Maaslin2 program?

4

Yes. You might find the tutorial helpful as well.