miaTime: A scalable R/Bioconductor framework for longitudinal microbiome analysis

Understanding how microbial communities change over time is essential for studying ecological stability, disease progression, and treatment response. Longitudinal microbiome studies enable these insights but pose analytical challenges, including repeated measures, missing data, batch effects, and the complexity of temporal modeling. miaTime is a new R/Bioconductor package designed to directly address these challenges. miaTime extends the mia (Microbiome Analysis) framework with a standardized, scalable approach for managing, preprocessing, and analyzing temporal microbiome data. Like other mia tools, it builds on TreeSummarizedExperiment (TreeSE), an advanced data structure for storing microbiome feature tables linked to phylogenetic trees. The package implements a suite of analysis methods tailored for time-series microbiome studies, including measures of community stability, detection of bimodal abundance patterns, identification of short-term changes, and quantification of stepwise and baseline divergence. Designed for full interoperability within the R/Bioconductor ecosystem, miaTime advances longitudinal microbiome research across clinical, environmental, and systems biology applications.