Logistic Tree Gaussian Processes (LoTGaP) for Microbiome Dynamics and Treatment Effects

Abstract

<p>With advancements in and increased access to next-generation sequencing technology, hospitals (such as Duke Medical Center) have started to track the microbiomes of at-risk patients over time, but at inconsistently measured points across patients. Modeling the trajectories of high-throughput microbiome data proves difficult, due to inconsistent data collection, as well as a collection of analytical obstacles such as compositional data, sparsity, high dimensionality, and phylogenetic covariance structure. As a result, few methods allow us to capture uncertainty in the microbiome over time using increasingly standard data collection and processing methods.</p><p>Here, we develop a novel hierarchical model to measure dynamics of the microbiome across cohorts of patients measured inconsistently, which we call logistic-tree Gaussian processes for the microbiome (LoTGaP). LoTGaP adds to the existing microbiome literature through (1) using Gaussian processes to flexibly estimate the evolution of the microbiome over a finite set of days to handle missing/inconsistently measured data, (2) transforming operational taxonomic units (OTUs) to their internal nodes on the phylogenetic tree to accelerate computation and preserve biological relationships, and (3) building functionality to estimate the influence of covariates on microbiome dynamics across patients, which can allow for hospitals to link treatment regimens to microbiome dynamics, or make direct connections between microbiome data and other measurements, such as demographic information. </p><p>We demonstrate that LoTGaP produces uncertainty bands that reflect both within-person variation over time and across-person variation while comparing favorably in computation time to existing methods that are narrower in scope.</p>