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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927166/ https://www.ncbi.nlm.nih.gov/pubmed/31870407 http://dx.doi.org/10.1186/s13059-019-1871-4 |
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author | Liao, Tianhua Wei, Yuchen Luo, Mingjing Zhao, Guo-Ping Zhou, Haokui |
author_facet | Liao, Tianhua Wei, Yuchen Luo, Mingjing Zhao, Guo-Ping Zhou, Haokui |
author_sort | Liao, Tianhua |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-6927166 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-69271662019-12-30 tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies Liao, Tianhua Wei, Yuchen Luo, Mingjing Zhao, Guo-Ping Zhou, Haokui Genome Biol Method 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. BioMed Central 2019-12-23 /pmc/articles/PMC6927166/ /pubmed/31870407 http://dx.doi.org/10.1186/s13059-019-1871-4 Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Method Liao, Tianhua Wei, Yuchen Luo, Mingjing Zhao, Guo-Ping Zhou, Haokui tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title_full | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title_fullStr | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title_full_unstemmed | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title_short | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
title_sort | tmap: an integrative framework based on topological data analysis for population-scale microbiome stratification and association studies |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927166/ https://www.ncbi.nlm.nih.gov/pubmed/31870407 http://dx.doi.org/10.1186/s13059-019-1871-4 |
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