Kernel-Penalized Regression for Analysis of Microbiome Data

Abstract

The analysis of human microbiome data is often based on dimension-reduced graphical displays and clustering derived from vectors of microbial abundances in each sample. Common to these ordination methods is the use of biologically motivated definitions of similarity. Principal coordinate analysis, in particular, is often performed using ecologically defined distances, allowing analyses to incorporate context-dependent, non-Euclidean structure. Here we describe how to take a step beyond ordination plots and incorporate this structure into high-dimensional penalized regression models. Within this framework, the estimate of a regression coefficient vector is obtained via the joint eigen properties of multiple similarity matrices, or kernels. This allows for multivariate regression models to incorporate both a matrix of microbial abundances and, for instance, a matrix of phylogenetically-informed similarities between the abundance profiles. Further, we show how this regression framework can be used to address the compositional nature of multivariate predictors comprised of relative abundances; that is, vectors whose entries sum to a constant. We illustrate this regression framework with several simulations using data from two recent studies on the gut and vaginal microbiome. We conclude with an application to our own data, where we also incorporate a significance test for the estimated coefficients that represent associations between microbial abundance and a response.