tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies

被引:0
作者
Tianhua Liao
Yuchen Wei
Mingjing Luo
Guo-Ping Zhao
Haokui Zhou
机构
[1] Chinese Academy of Sciences,Institute of Synthetic Biology, Shenzhen Institutes of Advanced Technology
[2] The Chinese University of Hong Kong,Department of Microbiology
[3] Chinese Academy of Sciences,Bio
[4] Chinese Academy of Sciences,Med Big Data Center, CAS Key Laboratory of Computational Biology, CAS
[5] Fudan University,MPG Partner Institute for Computational Biology, Shanghai Institute of Nutrition and Health
[6] Chinese National Human Genome Center,CAS
[7] The Chinese University of Hong Kong,Key Laboratory of Synthetic Biology, CAS Center for Excellence in Molecular Plant Sciences, Shanghai Institute of Plant Physiology and Ecology
[8] Prince of Wales Hospital,Department of Microbiology and Microbial Engineering, School of Life Sciences
来源
Genome Biology | / 20卷
关键词
Microbiome-wide association analysis; Topological data analysis; Population-scale microbiome; Microbiome stratification; Nonlinear association; Enterotype analysis;
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摘要
Untangling the complex variations of microbiome associated with large-scale host phenotypes or environment types challenges the currently available analytic methods. Here, we present tmap, an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies. The performance of tmap in detecting nonlinear patterns is validated by different scenarios of simulation, which clearly demonstrate its superiority over the most commonly used methods. Application of tmap to several population-scale microbiomes extensively demonstrates its strength in revealing microbiome-associated host or environmental features and in understanding the systematic interrelations among their association patterns. tmap is available at https://github.com/GPZ-Bioinfo/tmap.
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