biostudies-literatureYang LCenter for Individualized Medicine at Mayo ClinicNHGRI NIH HHSNational Institutes of HealthNIH HHSNIGMS NIH HHSNational Science Foundationbbac607https://www.ebi.ac.uk/biostudies/studies/S-EPMC9851339biostudies-literatureUnknown24(1)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 methodsBriefings in bioinformaticsBenchmarking differential abundance analysis methods for correlated microbiome sequencing data.PMC9851339R01 GM144351DMS 2113360R21 HG011662Yang LChen JfalseBenchmarking differential abundance analysis methods for correlated microbiome sequencing data.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 methods2023-01-01T00:00:00Z2023 Jan2026-04-28T03:14:09.347Z2025-04-04T21:55:06.424ZS-EPMC98513393661718710.1093/bib/bbac607