MaAsLin 3: refining and extending generalized multivariable linear models for meta-omic association discovery

Microbial community analysis typically involves determining which microbial features are associated with properties such as environmental or health phenotypes. This task is impeded by data characteristics, including sparsity (technical or biological) and compositionality. Here we introduce MaAsLin 3 (microbiome multivariable associations with linear models) to simultaneously identify both abundance and prevalence relationships in microbiome studies with modern, potentially complex designs. MaAsLin 3 can newly account for compositionality either experimentally (for example, quantitative PCR or spike-ins) or computationally, and it expands the range of testable biological hypotheses and covariate types. On a…

• NI
  National Institute of General Medical Sciences

  Award: T32GM135117
• NI
  National Institute of Allergy and Infectious Diseases

  Award: U19AI110820