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Microbiome interventions provide valuable data about microbial ecosystemstructure and dynamics. Despite their ubiquity in microbiome research, fewrigorous data analysis approaches are available. In this study, we extendtransfer function-based intervention analysis to the microbiome setting,drawing from advances in statistical learning and selective inference. Ourproposal supports the simulation of hypothetical intervention trajectories andFalse Discovery Rate-guaranteed selection of significantly perturbed taxa. Weexplore the properties of our approach through simulation and re-analyze threecontrasting microbiome studies. An R package, mbtransfer, is available athttps://go.wisc.edu/crj6k6. Notebooks to reproduce the simulation and casestudies can be found at https://go.wisc.edu/dxuibh andhttps://go.wisc.edu/emxv33.

Microbiome Intervention Analysis with Transfer Functions and Mirror Statistics