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Dynamic Interaction Network Inference From Longitudinal Microbiome Data

NCJ Number
253923
Author(s)
Jose Lugo-Martinez; Daniel Ruiz-Perez; Giri Narasimhan; Ziv Bar-Joseph
Date Published
2019
Length
8 pages
Annotation
This article presents a computational pipeline that enables the integration of data across individuals for the reconstruction of dynamic models from time series microbiome data.
Abstract
Several studies have focused on the microbiota living in environmental niches, including human body sites. In many of these studies, researchers collect longitudinal data with the goal of understanding both the composition of the microbiome and the interactions between the different taxa; however, analysis of such data is challenging and few methods have been developed to reconstruct dynamic models from time series microbiome data. In the current project, the pipeline starts by aligning the data collected for all individuals. The aligned profiles are then used to learn a dynamic Bayesian network, which represents causal relationships between taxa and clinical variables. Testing These methods on three longitudinal microbiome data sets indicates that this pipeline improves upon prior methods developed for this task. This article also discusses the biological insights provided by the models, which include several known and novel interactions. The extended CGBayesNets package is freely available under the MIT Open Source license agreement. The source code and documentation can be downloaded from https://github.com/jlugomar/longitudinal_microbiome_analysis_public. (publisher abstract modified)

Date Created: July 20, 2021