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Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible
Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these appr...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/ https://www.ncbi.nlm.nih.gov/pubmed/24699258 http://dx.doi.org/10.1371/journal.pcbi.1003531 |
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author | McMurdie, Paul J. Holmes, Susan |
author_facet | McMurdie, Paul J. Holmes, Susan |
author_sort | McMurdie, Paul J. |
collection | PubMed |
description | Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq. |
format | Online Article Text |
id | pubmed-3974642 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-39746422014-04-08 Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible McMurdie, Paul J. Holmes, Susan PLoS Comput Biol Research Article Current practice in the normalization of microbiome count data is inefficient in the statistical sense. For apparently historical reasons, the common approach is either to use simple proportions (which does not address heteroscedasticity) or to use rarefying of counts, even though both of these approaches are inappropriate for detection of differentially abundant species. Well-established statistical theory is available that simultaneously accounts for library size differences and biological variability using an appropriate mixture model. Moreover, specific implementations for DNA sequencing read count data (based on a Negative Binomial model for instance) are already available in RNA-Seq focused R packages such as edgeR and DESeq. Here we summarize the supporting statistical theory and use simulations and empirical data to demonstrate substantial improvements provided by a relevant mixture model framework over simple proportions or rarefying. We show how both proportions and rarefied counts result in a high rate of false positives in tests for species that are differentially abundant across sample classes. Regarding microbiome sample-wise clustering, we also show that the rarefying procedure often discards samples that can be accurately clustered by alternative methods. We further compare different Negative Binomial methods with a recently-described zero-inflated Gaussian mixture, implemented in a package called metagenomeSeq. We find that metagenomeSeq performs well when there is an adequate number of biological replicates, but it nevertheless tends toward a higher false positive rate. Based on these results and well-established statistical theory, we advocate that investigators avoid rarefying altogether. We have provided microbiome-specific extensions to these tools in the R package, phyloseq. Public Library of Science 2014-04-03 /pmc/articles/PMC3974642/ /pubmed/24699258 http://dx.doi.org/10.1371/journal.pcbi.1003531 Text en © 2014 McMurdie, Holmes http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article McMurdie, Paul J. Holmes, Susan Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title | Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title_full | Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title_fullStr | Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title_full_unstemmed | Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title_short | Waste Not, Want Not: Why Rarefying Microbiome Data Is Inadmissible |
title_sort | waste not, want not: why rarefying microbiome data is inadmissible |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3974642/ https://www.ncbi.nlm.nih.gov/pubmed/24699258 http://dx.doi.org/10.1371/journal.pcbi.1003531 |
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