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

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...

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Bibliographic Details
Published in:	Genome Biology 2019-12, Vol.20 (1), p.293-19, Article 293
Main Authors:	Liao, Tianhua, Wei, Yuchen, Luo, Mingjing, Zhao, Guo-Ping, Zhou, Haokui
Format:	Article
Language:	eng
Subjects:	Algorithms Clustering Data analysis Data Mining Datasets Enterotype analysis Humans Metadata Method Methods Microbiome stratification Microbiome-wide association analysis Microbiomes Microbiota Nonlinear association Phenotypes Population Population-scale microbiome Principal components analysis Regression analysis Software Topological data analysis
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Summary:	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.
ISSN:	1474-760X 1474-7596 1474-760X