Identifying prognostic pairwise relationships among bacterial species in microbiome studies.

Abstract

In recent literature, the human microbiome has been shown to have a major influence on human health. To investigate this impact, scientists study the composition and abundance of bacterial species, commonly using 16S rRNA gene sequencing, among patients with and without a disease or condition. Methods for such investigations to date have focused on the association between individual bacterium and an outcome, and higher-order pairwise relationships or interactions among bacteria are often avoided due to the substantial increase in dimension and the potential for spurious correlations. However, overlooking such relationships ignores the environment of the microbiome, where there is dynamic cooperation and competition among bacteria. We present a method for identifying and ranking pairs of bacteria that have a differential dichotomized relationship across outcomes. Our approach, implemented in an R package PairSeek, uses the stability selection framework with data-driven dichotomized forms of the pairwise relationships. We illustrate the properties of the proposed method using a published oral cancer data set and a simulation study.