Inferring Microbial Interactions from Metagenomic Time-series Using Prior Biological Knowledge

Due to the recent advances in modern metagenomics sequencing methods, it becomes possible to directly analyze the microbial communities within human body. To understand how microbial communities adapt, develop, and interact over time with the human body and the surrounding environment, a critical step is the inference of interactions among different microbes directly from sequencing data. However, metagenomics data is both compositional and highly dimensional in nature. Consequently, new approaches that can accurately and robustly estimate the interactions among various microbe species are needed to analyze such data. To this end, we propose a novel framework called Microbial Time-series Prior Lasso (MTPLasso) which integrates sparse linear regression with microbial co-occurrences and associations obtained from scientific literature and cross-sectional metagenomics data. We show that MTPLasso outperforms existing models in terms of precision and recall rates, as well as the accuracy in inferring the interaction types. Finally, the interaction networks we infer from human gut data demonstrate credible results when compared against real data.