LOCOM: A logistic regression model for testing differential abundance in compositional microbiome data with false discovery rate control.

ABSTRACT: Compositional analysis is based on the premise that a relatively small proportion of taxa are differentially abundant, while the ratios of the relative abundances of the remaining taxa remain unchanged. Most existing methods use log-transformed data, but log-transformation of data with pervasive zero counts is problematic, and these methods cannot always control the false discovery rate (FDR). Further, high-throughput microbiome data such as 16S amplicon or metagenomic sequencing are subject to experimental biases that are introduced in every step of the experimental workflow. McLaren et al. [eLife 8, e46923 (2019)] have recently proposed a model for how these biases affect relative abundance data. Motivated by this model, we show that the odds ratios in a logistic regression compar

SUBMITTER: Hu Y 

PROVIDER: S-EPMC9335309 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature