phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.

the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued...

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Main Authors: Paul J McMurdie, Susan Holmes
Format: Article
Language:English
Published: Public Library of Science (PLoS) 2013-01-01
Series:PLoS ONE
Online Access:http://europepmc.org/articles/PMC3632530?pdf=render
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spelling doaj-c4c4dd0760f34bb9b636ec93d156030c2020-11-24T22:07:56ZengPublic Library of Science (PLoS)PLoS ONE1932-62032013-01-0184e6121710.1371/journal.pone.0061217phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.Paul J McMurdieSusan Holmesthe analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.http://europepmc.org/articles/PMC3632530?pdf=render
collection DOAJ
language English
format Article
sources DOAJ
author Paul J McMurdie
Susan Holmes
spellingShingle Paul J McMurdie
Susan Holmes
phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
PLoS ONE
author_facet Paul J McMurdie
Susan Holmes
author_sort Paul J McMurdie
title phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
title_short phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
title_full phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
title_fullStr phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
title_full_unstemmed phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data.
title_sort phyloseq: an r package for reproducible interactive analysis and graphics of microbiome census data.
publisher Public Library of Science (PLoS)
series PLoS ONE
issn 1932-6203
publishDate 2013-01-01
description the analysis of microbial communities through dna sequencing brings many challenges: the integration of different types of data with methods from ecology, genetics, phylogenetics, multivariate statistics, visualization and testing. With the increased breadth of experimental designs now being pursued, project-specific statistical analyses are often needed, and these analyses are often difficult (or impossible) for peer researchers to independently reproduce. The vast majority of the requisite tools for performing these analyses reproducibly are already implemented in R and its extensions (packages), but with limited support for high throughput microbiome census data.Here we describe a software project, phyloseq, dedicated to the object-oriented representation and analysis of microbiome census data in R. It supports importing data from a variety of common formats, as well as many analysis techniques. These include calibration, filtering, subsetting, agglomeration, multi-table comparisons, diversity analysis, parallelized Fast UniFrac, ordination methods, and production of publication-quality graphics; all in a manner that is easy to document, share, and modify. We show how to apply functions from other R packages to phyloseq-represented data, illustrating the availability of a large number of open source analysis techniques. We discuss the use of phyloseq with tools for reproducible research, a practice common in other fields but still rare in the analysis of highly parallel microbiome census data. We have made available all of the materials necessary to completely reproduce the analysis and figures included in this article, an example of best practices for reproducible research.The phyloseq project for R is a new open-source software package, freely available on the web from both GitHub and Bioconductor.
url http://europepmc.org/articles/PMC3632530?pdf=render
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