Benchmarking differential abundance analysis methods for correlated microbiome sequencing data

Lu Yang, Jun Chen

Research output: Contribution to journalReview articlepeer-review


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.

Original languageEnglish (US)
Article numberbbac607
JournalBriefings in bioinformatics
Issue number1
StatePublished - Jan 1 2023


  • differential abundance analysis
  • longitudinal
  • matched-pair
  • metagenomics
  • microbiome
  • repeated sampling

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology


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