A novel normalization and differential abundance test framework for microbiome data.

ABSTRACT:

Motivation

Microbial communities have been proved to have close relationship with many diseases. The identification of differentially abundant microbial species is clinically meaningful for finding disease-related pathogenic or probiotic bacteria. However, certain characteristics of microbiome data have hurdled the accuracy and effectiveness of differential abundance analysis. The abundances or counts of microbiome species are usually on different scales and exhibit zero-inflation and over-dispersion. Normalization is a crucial step before the differential abundance test. However, existing normalization methods typically try to adjust counts on different scales to a common scale by constructing size factors with the assumption that count distributions across samples are equivalent

SUBMITTER: Ma Y 

PROVIDER: S-EPMC7332570 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature