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MetaTopics: an integration tool to analyze microbial community profile by topic model

Jifang Yan¹, Guohui Chuai¹, Tao Qi¹

¹Department of Central Laboratory, Shanghai Tenth People's Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China.

|February 16, 2017

Abstract

Background:

Deciphering taxonomical structures based on high dimensional sequencing data is still challenging in metagenomics study. Moreover, the common workflow processed in this field fails to identify microbial communities and their effect on a specific disease status. Even the relationships and interactions between different bacteria in a microbial community keep unknown.

Results:

MetaTopics can efficiently extract the latent microbial communities which reflect the intrinsic relations or interactions among several major microbes. Furthermore, a quantitative measurement, Quetelet Index, is defined to estimate the influence of a latent sub-community on a certain disease status for given samples. An analysis of our in-house oral metagenomics data and public gut microbe data was presented to demonstrate the application and usefulness of MetaTopics. To preset a user-friendly R package, we have built a dedicated website, https://github.com/bm2-lab/MetaTopics , which includes free downloads, detailed tutorials and illustration examples.

Conclusions:

MetaTopics is the first interactive R package to integrate the state-of-arts topic model derived from statistical learning community to analyze and visualize the metagenomics taxonomy data.

Keywords:

Disease status Metagenomics Microbial community R Topic model

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