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

BMC Genomics

Research

Tao Qi¹  Chi Zhou¹  Yifei Yu¹  Guohui Chuai¹  Jing Yang¹  Qi Liu¹  Jifang Yan¹  Chenyu Zhu¹  Cong Shi²  Fangyang Shao²  Yuan He²  Ning Kang³ 

[1] Department of Central Laboratory, Shanghai Tenth People’s Hospital, School of Life Sciences and Technology, Tongji University, Shanghai, China;Department of oral medicine, Shanghai Engineering Research Center of Tooth Restoration and Regeneration, School of Stomatology, Tongji University, Shanghai, China;School of Life Science and Technology, Huazhong University of Science and Technology, Wuhan, China;

关键词: Metagenomics;    R;    Topic model;    Microbial community;    Disease status;   

DOI  :  10.1186/s12864-016-3257-2

来源: Springer

【 摘 要 】

BackgroundDeciphering 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.ResultsMetaTopics 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.ConclusionsMetaTopics 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.

【 授权许可】

CC BY   
© The Author(s). 2017