Waste not, want not: why rarefying microbiome data is inadmissible.
Paul J. McMurdie,Susan Holmes +1 more
Reads0
Chats0
TLDR
It is advocated that investigators avoid rarefying altogether and supported statistical theory is provided that simultaneously accounts for library size differences and biological variability using an appropriate mixture model.Abstract:
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.read more
Citations
More filters
Journal ArticleDOI
Breast tissue, oral and urinary microbiomes in breast cancer
Hannah Wang,Hannah Wang,Jessica Altemus,Farshad Niazi,Holly C. Green,Benjamin C. Calhoun,Charles D. Sturgis,Stephen R. Grobmyer,Charis Eng +8 more
TL;DR: It is hypothesized that cancerous breast tissue is associated with a microbiomic profile distinct from that of benign breast tissue, and that microbiomes of more distant sites, the oral cavity and urinary tract, will reflect dysbiosis as well.
Journal ArticleDOI
Dysbiosis of gut microbiota in a selected population of Parkinson's patients
Daniele Pietrucci,Rocco Cerroni,Valeria Unida,Alessio Farcomeni,Mariangela Pierantozzi,Nicola Biagio Mercuri,Silvia Biocca,Alessandro Stefani,Alessandro Desideri +8 more
TL;DR: Functional predictions suggest changes in pathways favoring a pro-inflammatory environment in the gastrointestinal tract, and a reduction in the biosynthesis of amino acids acting as precursors of physiological transmitters in Parkinson's disease.
Journal ArticleDOI
Dynamic Human Environmental Exposome Revealed by Longitudinal Personal Monitoring
TL;DR: It is demonstrated that human exposomes are diverse, dynamic, spatiotemporally-driven interaction networks with the potential to impact human health.
Journal ArticleDOI
Different Bacterial Populations Associated with the Roots and Rhizosphere of Rice Incorporate Plant-Derived Carbon
TL;DR: Microorganisms associated with the roots of plants have an important function in plant growth and in soil carbon sequestration, and a proportion of the active microbial community on the roots greater than that in the rhizosphere incorporated plant-derived carbon within the time frame of the experiment.
Journal ArticleDOI
Body size determines soil community assembly in a tropical forest.
Lucie Zinger,Pierre Taberlet,Heidy Schimann,Aurélie Bonin,Frédéric Boyer,Marta De Barba,Philippe Gaucher,Ludovic Gielly,Charline Giguet-Covex,Amaia Iribar,Maxime Réjou-Méchain,Gilles Rayé,Delphine Rioux,Vincent Schilling,Blaise Tymen,Jérôme Viers,Cyril Zouiten,Wilfried Thuiller,Eric Coissac,Jérôme Chave +19 more
TL;DR: It is shown that body size, which determines the scale at which an organism perceives its environment, predicted the community assembly across taxonomic groups, with soil mesofauna assemblages being more stochastic than microbial ones.
References
More filters
Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
Journal ArticleDOI
Controlling the false discovery rate: a practical and powerful approach to multiple testing
Yoav Benjamini,Yosef Hochberg +1 more
TL;DR: In this paper, a different approach to problems of multiple significance testing is presented, which calls for controlling the expected proportion of falsely rejected hypotheses -the false discovery rate, which is equivalent to the FWER when all hypotheses are true but is smaller otherwise.
Book
ggplot2: Elegant Graphics for Data Analysis
TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Journal ArticleDOI
edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.
TL;DR: EdgeR as mentioned in this paper is a Bioconductor software package for examining differential expression of replicated count data, which uses an overdispersed Poisson model to account for both biological and technical variability and empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference.
Journal ArticleDOI
QIIME allows analysis of high-throughput community sequencing data.
J. Gregory Caporaso,Justin Kuczynski,Jesse Stombaugh,Kyle Bittinger,Frederic D. Bushman,Elizabeth K. Costello,Noah Fierer,Antonio Gonzalez Peña,Julia K. Goodrich,Jeffrey I. Gordon,Gavin A. Huttley,Scott T. Kelley,Dan Knights,Jeremy E. Koenig,Ruth E. Ley,Catherine A. Lozupone,Daniel McDonald,Brian D. Muegge,Meg Pirrung,Jens Reeder,Joel Sevinsky,Peter J. Turnbaugh,William A. Walters,Jeremy Widmann,Tanya Yatsunenko,Jesse R. Zaneveld,Rob Knight,Rob Knight +27 more
TL;DR: An overview of the analysis pipeline and links to raw data and processed output from the runs with and without denoising are provided.
Related Papers (5)
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2
QIIME allows analysis of high-throughput community sequencing data.
J. Gregory Caporaso,Justin Kuczynski,Jesse Stombaugh,Kyle Bittinger,Frederic D. Bushman,Elizabeth K. Costello,Noah Fierer,Antonio Gonzalez Peña,Julia K. Goodrich,Jeffrey I. Gordon,Gavin A. Huttley,Scott T. Kelley,Dan Knights,Jeremy E. Koenig,Ruth E. Ley,Catherine A. Lozupone,Daniel McDonald,Brian D. Muegge,Meg Pirrung,Jens Reeder,Joel Sevinsky,Peter J. Turnbaugh,William A. Walters,Jeremy Widmann,Tanya Yatsunenko,Jesse R. Zaneveld,Rob Knight,Rob Knight +27 more
Introducing mothur: Open-Source, Platform-Independent, Community-Supported Software for Describing and Comparing Microbial Communities
Patrick D. Schloss,Patrick D. Schloss,Sarah L. Westcott,Sarah L. Westcott,Thomas Ryabin,Justine R. Hall,Martin Hartmann,Emily B. Hollister,Ryan A. Lesniewski,Brian B. Oakley,Donovan H. Parks,Courtney J. Robinson,Jason W. Sahl,Blaz Stres,Gerhard G. Thallinger,David J. Van Horn,Carolyn F. Weber +16 more