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

ABSTRACT:

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 unsupervised learning-based microbial interaction inference method using Bayesian estimation (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.

SUBMITTER: Hosoda S

PROVIDER: S-EPMC8275348 | biostudies-literature |

REPOSITORIES: biostudies-literature

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