Work

Benchmarking differential abundance analysis methods for correlated microbiome sequencing data

Year: 2022

Type: article

Abstract: Differential abundance analysis (DAA) is one central statistical task in microbiome data analysis. A robust and powerful DAA tool can help identify highly confident microbial candidates for further biological validation. Current microbiome studies frequently generate correlated samples from different microbiome sampling schemes such as spatial and temporal sampling. In the past decade, a number of DAA tools for correlated microbiome data (DAA-c) have been proposed. Disturbingly, different DAA-c tools could sometimes produce quite discordant results. To recommend the best practice to the field, we performed the first comprehensive evaluation of existing DAA-c tools using real data-based simulations. Overall, the linear model-based methods LinDA, MaAsLin2 and LDM are more robust than methods based on generalized linear models. The LinDA method is the only method that maintains reasonable performance in the presence of strong compositional effects.

Source: Briefings in Bioinformatics

DOI: 10.1093/bib/bbac607

Authors: Lu Yang, Jun Chen

Institution: Mayo Clinic

Language: English

Topic: Gut microbiota and health

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: green

Funders: National Science Foundation, National Institutes of Health, National Human Genome Research Institute, National Institute of General Medical Sciences

Awards: Collaborative Research: New Statistical Methods for Microbiome Data Analysis, G5520316217, Methods for Analysis of Genomic Data with Auxiliary Information, Methods for microbiome compositional data