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VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition
BACKGROUND: Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as da...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684964/ https://www.ncbi.nlm.nih.gov/pubmed/33228810 http://dx.doi.org/10.1186/s40168-020-00934-6 |
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author | France, Michael T. Ma, Bing Gajer, Pawel Brown, Sarah Humphrys, Michael S. Holm, Johanna B. Waetjen, L. Elaine Brotman, Rebecca M. Ravel, Jacques |
author_facet | France, Michael T. Ma, Bing Gajer, Pawel Brown, Sarah Humphrys, Michael S. Holm, Johanna B. Waetjen, L. Elaine Brotman, Rebecca M. Ravel, Jacques |
author_sort | France, Michael T. |
collection | PubMed |
description | BACKGROUND: Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs. RESULTS: We developed VALENCIA (VAginaL community state typE Nearest CentroId clAssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA. CONCLUSION: VALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s40168-020-00934-6. |
format | Online Article Text |
id | pubmed-7684964 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-76849642020-11-25 VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition France, Michael T. Ma, Bing Gajer, Pawel Brown, Sarah Humphrys, Michael S. Holm, Johanna B. Waetjen, L. Elaine Brotman, Rebecca M. Ravel, Jacques Microbiome Research BACKGROUND: Taxonomic profiles of vaginal microbial communities can be sorted into a discrete number of categories termed community state types (CSTs). This approach is advantageous because collapsing a hyper-dimensional taxonomic profile into a single categorical variable enables efforts such as data exploration, epidemiological studies, and statistical modeling. Vaginal communities are typically assigned to CSTs based on the results of hierarchical clustering of the pairwise distances between samples. However, this approach is problematic because it complicates between-study comparisons and because the results are entirely dependent on the particular set of samples that were analyzed. We sought to standardize and advance the assignment of samples to CSTs. RESULTS: We developed VALENCIA (VAginaL community state typE Nearest CentroId clAssifier), a nearest centroid-based tool which classifies samples based on their similarity to a set of reference centroids. The references were defined using a comprehensive set of 13,160 taxonomic profiles from 1975 women in the USA. This large dataset allowed us to comprehensively identify, define, and characterize vaginal CSTs common to reproductive age women and expand upon the CSTs that had been defined in previous studies. We validated the broad applicability of VALENCIA for the classification of vaginal microbial communities by using it to classify three test datasets which included reproductive age eastern and southern African women, adolescent girls, and a racially/ethnically and geographically diverse sample of postmenopausal women. VALENCIA performed well on all three datasets despite the substantial variations in sequencing strategies and bioinformatics pipelines, indicating its broad application to vaginal microbiota. We further describe the relationships between community characteristics (vaginal pH, Nugent score) and participant demographics (race, age) and the CSTs defined by VALENCIA. CONCLUSION: VALENCIA provides a much-needed solution for the robust and reproducible assignment of vaginal community state types. This will allow unbiased analysis of both small and large vaginal microbiota datasets, comparisons between datasets and meta-analyses that combine multiple datasets. SUPPLEMENTARY INFORMATION: Supplementary information accompanies this paper at 10.1186/s40168-020-00934-6. BioMed Central 2020-11-23 /pmc/articles/PMC7684964/ /pubmed/33228810 http://dx.doi.org/10.1186/s40168-020-00934-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research France, Michael T. Ma, Bing Gajer, Pawel Brown, Sarah Humphrys, Michael S. Holm, Johanna B. Waetjen, L. Elaine Brotman, Rebecca M. Ravel, Jacques VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title | VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title_full | VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title_fullStr | VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title_full_unstemmed | VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title_short | VALENCIA: a nearest centroid classification method for vaginal microbial communities based on composition |
title_sort | valencia: a nearest centroid classification method for vaginal microbial communities based on composition |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7684964/ https://www.ncbi.nlm.nih.gov/pubmed/33228810 http://dx.doi.org/10.1186/s40168-020-00934-6 |
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