mbtransfer: Microbiome intervention analysis using transfer functions and mirror statistics

Time series studies of microbiome interventions provide valuable data about microbial ecosystem structure. Unfortunately, existing models of microbial community dynamics have limited temporal memory and expressivity, relying on Markov or linearity assumptions. To address this, we introduce a new class of models based on transfer functions. These models learn impulse responses, capturing the potentially delayed effects of environmental changes on the microbial community. This allows us to simulate trajectories under hypothetical interventions and select significantly perturbed taxa with False Discovery Rate guarantees. Through simulations, we show that our approach effectively reduces forecasting errors compared to strong baselines and accurately pinpoints taxa of interest. Our case studies highlight the interpretability of the resulting differential response trajectories. An R package, mbtransfer, and notebooks to replicate the simulation and case studies are provided.Author summary: Effectively controlling dynamic microbiomes has remained a major research challenge, primarily due to the interdependence between microbes and their sensitivity to environmental change. Tackling this challenge would advance microbiome engineering, with significant implications for healthcare, agriculture, and conservation. We introduce a flexible and statistically-principled approach to modeling microbe-microbe and microbe-environment relationships. We illustrate the methodology on case studies using microbiome time series datasets related to precision nutrition and women’s health. We have released a software package, mbtransfer, to allow easy implementation of the methodology in other contexts where it is important to quantify intervention effects in temporally sampled data.