Design, processing, and modeling for longitudinal multiomics microbiome data

Longitudinal multiomics studies can reveal mechanisms underlying microbiome dynamics. Though gathering such data has become increasingly accessible, challenges remain in experimental design, data processing, and interaction modeling. This mini-review surveys practical approaches for analyzing longitudinal multiomics microbiome data. We provide an overview of fundamental questions these experimental designs can address, discuss concepts for reducing confounding, review tools for data management, and describe statistical and machine learning methods for identifying interactions across time and biological layers. We conclude with emerging trends and open problems.