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

• Type of Material: Article
• Authors: Yingtian Hu, Emory University; Glen Satten, Emory University; Yijuan Hu, Emory University
• Language: English
• Date: 2022-07-26
• Publisher: PNAS.
• Publication Version: Final Published Version
• Copyright Statement: © 2022 the Author(s). Published by PNAS.
• License: Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International
• Final Published Version (URL): http://dx.doi.org/10.1073/pnas.2122788119
• Title of Journal or Parent Work: Proceedings of the National Academy of Sciences of the United States of America
• Volume: 119
• Issue: 30
• Start Page: e2122788119
• End Page: e2122788119
• Keywords: Logistic Models; experimental bias; Microbiota; Metagenomics; logit model; pseudocount; sparse data; Sequence Analysis; log ratio
• Research Categories: Health Sciences, Obstetrics and Gynecology; Biology, Biostatistics

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 comparing counts in two taxa are invariant to experimental biases. With this motivation, we propose logistic compositional analysis (LOCOM), a robust logistic regression approach to compositional analysis, that does not require pseudocounts. Inference is based on permutation to account for overdispersion and small sample sizes. Traits can be either binary or continuous, and adjustment for confounders is supported. Our simulations indicate that LOCOM always preserved FDR and had much improved sensitivity over existing methods. In contrast, analysis of composition of microbiomes (ANCOM) and ANCOM with bias correction (ANCOM-BC)/ANOVA-Like Differential Expression tool (ALDEx2) had inflated FDR when the effect sizes were small and large, respectively. Only LOCOM was robust to experimental biases in every situation. The flexibility of our method for a variety of microbiome studies is illustrated by the analysis of data from two microbiome studies. Our R package LOCOM is publicly available.