Inferring directional relationships in microbial communities using signed Bayesian networks
Musfiqur Sazal, Kalai Mathee, Daniel Ruiz-Perez, Trevor Cickovski, Giri Narasimhan
Abstract
BACKGROUND: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. RESULTS: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. CONCLUSIONS: BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order.