Yan Jifang, Chuai Guohui, Qi Tao, Shao Fangyang, Zhou Chi, Zhu Chenyu, Yang Jing, Yu Yifei, Shi Cong, Kang Ning, He Yuan, Liu Qi
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.
BMC Genomics. 2017 Jan 25;18(Suppl 1):962. doi: 10.1186/s12864-016-3257-2.
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.
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.
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.
在宏基因组学研究中，基于高维测序数据解析分类结构仍然具有挑战性。此外，该领域常用的工作流程无法识别微生物群落及其对特定疾病状态的影响。甚至微生物群落中不同细菌之间的关系和相互作用也仍然未知。
MetaTopics可以有效地提取潜在的微生物群落，这些群落反映了几种主要微生物之间的内在关系或相互作用。此外，还定义了一种定量测量方法——凯特勒指数，以估计潜在子群落在给定样本中对特定疾病状态的影响。通过对我们内部的口腔宏基因组学数据和公共肠道微生物数据进行分析，展示了MetaTopics的应用和实用性。为了预设一个用户友好的R包，我们建立了一个专门的网站https://github.com/bm2-lab/MetaTopics，其中包括免费下载、详细教程和示例。
MetaTopics是第一个交互式R包，它集成了来自统计学习社区的先进主题模型，用于分析和可视化宏基因组学分类数据。
