Identifying prognostic pairwise relationships among bacterial species in microbiome studies

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.Author summary: Within an ecological system, microbial communities represent complex relationships between bacteria, where they co-exist and interact with each other in multiple ways including cooperation and competition. Most existing statistical tools for examining the association between microbiota and a disease state, such as individuals with and without cancer, focus on individual bacterium in isolation, ignoring the dynamic environment in which it lives. In this manuscript, we propose an algorithm for assessing the association between pairs of bacteria and a disease state. The approach provides a mechanism to rank pairs of bacteria, from pairs with the most evidence of an association with the disease state to the least amount of evidence. This ranking helps generate hypotheses and prioritize bacteria for further investigation. We illustrate the algorithm using a publicly available data set of oral cancer patients.