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tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R
BACKGROUND: The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852128/ https://www.ncbi.nlm.nih.gov/pubmed/33526006 http://dx.doi.org/10.1186/s12859-021-03967-2 |
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author | Carpenter, Charlie M. Frank, Daniel N. Williamson, Kayla Arbet, Jaron Wagner, Brandie D. Kechris, Katerina Kroehl, Miranda E. |
author_facet | Carpenter, Charlie M. Frank, Daniel N. Williamson, Kayla Arbet, Jaron Wagner, Brandie D. Kechris, Katerina Kroehl, Miranda E. |
author_sort | Carpenter, Charlie M. |
collection | PubMed |
description | BACKGROUND: The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. RESULTS: We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. CONCLUSIONS: tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets. |
format | Online Article Text |
id | pubmed-7852128 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-78521282021-02-03 tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R Carpenter, Charlie M. Frank, Daniel N. Williamson, Kayla Arbet, Jaron Wagner, Brandie D. Kechris, Katerina Kroehl, Miranda E. BMC Bioinformatics Software BACKGROUND: The drive to understand how microbial communities interact with their environments has inspired innovations across many fields. The data generated from sequence-based analyses of microbial communities typically are of high dimensionality and can involve multiple data tables consisting of taxonomic or functional gene/pathway counts. Merging multiple high dimensional tables with study-related metadata can be challenging. Existing microbiome pipelines available in R have created their own data structures to manage this problem. However, these data structures may be unfamiliar to analysts new to microbiome data or R and do not allow for deviations from internal workflows. Existing analysis tools also focus primarily on community-level analyses and exploratory visualizations, as opposed to analyses of individual taxa. RESULTS: We developed the R package “tidyMicro” to serve as a more complete microbiome analysis pipeline. This open source software provides all of the essential tools available in other popular packages (e.g., management of sequence count tables, standard exploratory visualizations, and diversity inference tools) supplemented with multiple options for regression modelling (e.g., negative binomial, beta binomial, and/or rank based testing) and novel visualizations to improve interpretability (e.g., Rocky Mountain plots, longitudinal ordination plots). This comprehensive pipeline for microbiome analysis also maintains data structures familiar to R users to improve analysts’ control over workflow. A complete vignette is provided to aid new users in analysis workflow. CONCLUSIONS: tidyMicro provides a reliable alternative to popular microbiome analysis packages in R. We provide standard tools as well as novel extensions on standard analyses to improve interpretability results while maintaining object malleability to encourage open source collaboration. The simple examples and full workflow from the package are reproducible and applicable to external data sets. BioMed Central 2021-02-01 /pmc/articles/PMC7852128/ /pubmed/33526006 http://dx.doi.org/10.1186/s12859-021-03967-2 Text en © The Author(s) 2021 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 | Software Carpenter, Charlie M. Frank, Daniel N. Williamson, Kayla Arbet, Jaron Wagner, Brandie D. Kechris, Katerina Kroehl, Miranda E. tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title | tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title_full | tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title_fullStr | tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title_full_unstemmed | tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title_short | tidyMicro: a pipeline for microbiome data analysis and visualization using the tidyverse in R |
title_sort | tidymicro: a pipeline for microbiome data analysis and visualization using the tidyverse in r |
topic | Software |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7852128/ https://www.ncbi.nlm.nih.gov/pubmed/33526006 http://dx.doi.org/10.1186/s12859-021-03967-2 |
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