Inference of microbial interactions from time series data using vector autoregression model

Microbial interaction, such as species competition and symbiotic relationships, plays important role to enable microorganisms to survive by establishing a homeostasis between microbial neighbors and local environments. Thanks to the recent accumulation of large-scale high-throughput sequencing data of complex microbial communities, there are increasing interests in identifying microbial interactions. Computational methods for microbial interactions inference are currently focused on the similarity among microbial individuals (i.e. cooccurrence and correlation patterns), however, less methods considered the dynamics of a single complex community over time. In this paper, we propose to use a multivariate statistical method - Multivariate Vector Autoregression (MVAR) to infer dynamic microbial interactions from the time series of human gut microbiomes. Specifically, we apply MVAR model on a time series data of human gut microbiomes which were treated with repeated antibiotics. The referred microbial interactions identify novel interactions which may provide a novel complementary to similarity or correlation-based methods.