Treffer: Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model.

Motivation Accumulating evidence has highlighted the importance of microbial interaction networks. Methods have been developed for estimating microbial interaction networks, of which the generalized Lotka–Volterra equation (gLVE)-based method can estimate a directed interaction network. The previous gLVE-based method for estimating microbial interaction networks did not consider time-varying interactions. Results In this study, we developed u nsupervised learning-based m icrobial i nteraction inference method using Ba yesian es t imati o n (Umibato), a method for estimating time-varying microbial interactions. The Umibato algorithm comprises Gaussian process regression (GPR) and a new Bayesian probabilistic model, the continuous-time regression hidden Markov model (CTRHMM). Growth rates are estimated by GPR, and interaction networks are estimated by CTRHMM. CTRHMM can estimate time-varying interaction networks using interaction states, which are defined as hidden variables. Umibato outperformed the existing methods on synthetic datasets. In addition, it yielded reasonable estimations in experiments on a mouse gut microbiota dataset, thus providing novel insights into the relationship between consumed diets and the gut microbiota. Availability and implementation The C++ and python source codes of the Umibato software are available at https://github.com/shion-h/Umibato. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

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