Hierarchical non-negative matrix factorization using clinical information for microbial communities

BMC Genomics

Kinji Ohno¹  Ko Abe²  Teppei Shimamura³  Masaaki Hirayama⁴ 

[1] Division of Neurogenetics, Center for Neurological Diseases and Cancer, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, 4668550, Nagoya, Japan;Division of Systems Biology, Nagoya University Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, 4668550, Nagoya, Japan;Division of Systems Biology, Nagoya university Graduate School of Medicine, 65 Tsurumai-cho, Showa-ku, 4668550, Nagoya, Japan;School of Health Sciences, Nagoya University Graduate School of Medicine, 1-1-20 Daiko-Minami, Higashi-ku, 61-8873, Nagoya, Japan;

关键词: Metagenomics;    Non-negative matrix factorization;    Bayesian hierarchical modeling;   

DOI  :  10.1186/s12864-021-07401-y

来源: Springer

【 摘 要 】

BackgroundThe human microbiome forms very complex communities that consist of hundreds to thousands of different microorganisms that not only affect the host, but also participate in disease processes. Several state-of-the-art methods have been proposed for learning the structure of microbial communities and to investigate the relationship between microorganisms and host environmental factors. However, these methods were mainly designed to model and analyze single microbial communities that do not interact with or depend on other communities. Such methods therefore cannot comprehend the properties between interdependent systems in communities that affect host behavior and disease processes.ResultsWe introduce a novel hierarchical Bayesian framework, called BALSAMICO (BAyesian Latent Semantic Analysis of MIcrobial COmmunities), which uses microbial metagenome data to discover the underlying microbial community structures and the associations between microbiota and their environmental factors. BALSAMICO models mixtures of communities in the framework of nonnegative matrix factorization, taking into account environmental factors. We proposes an efficient procedure for estimating parameters. A simulation then evaluates the accuracy of the estimated parameters. Finally, the method is used to analyze clinical data. In this analysis, we successfully detected bacteria related to colorectal cancer.ConclusionsThese results show that the method not only accurately estimates the parameters needed to analyze the connections between communities of microbiota and their environments, but also allows for the effective detection of these communities in real-world circumstances.

【 授权许可】

CC BY