Waste not, want not: why rarefying microbiome data is inadmissible.
Paul J. McMurdie,Susan Holmes +1 more
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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
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Generalized joint attribute modeling for biodiversity analysis: median-zero, multivariate, multifarious data
TL;DR: A generalized joint attribute model (GJAM) is developed, a probabilistic framework that readily applies to data that are combinations of presence-absence, ordinal, continuous, discrete, composition, zero-inflated, and censored, and it shows that the environment can be inverse predicted from the joint distribution of species.
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Diet, Environments, and Gut Microbiota. A Preliminary Investigation in Children Living in Rural and Urban Burkina Faso and Italy
Carlotta De Filippo,Monica Di Paola,Matteo Ramazzotti,Davide Albanese,Giuseppe Pieraccini,Elena Banci,Franco Miglietta,Duccio Cavalieri,Duccio Cavalieri,Paolo Lionetti +9 more
TL;DR: It is observed that when foods of animal origin, those rich in fat and simple sugars are introduced into a traditional African diet, composed of cereals, legumes and vegetables, the gut microbiota profiles changes, and there is a progressive reduction of SCFAs measured by gas chromatography–mass spectrometry, in urban populations, especially in Italian children, respect to rural ones.
Journal ArticleDOI
Defining Dysbiosis for a Cluster of Chronic Diseases.
TL;DR: Results of the cross-sectional analysis suggest that antibiotic-driven loss of microbial diversity may increase the risk for Chronic disease, however, longitudinal studies are needed to confirm the causative effect of diversity loss for chronic disease risk.
Journal ArticleDOI
Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes
Miklós Bálint,Mohammad Bahram,Mohammad Bahram,A. Murat Eren,A. Murat Eren,Karoline Faust,Jed A. Fuhrman,Björn D. Lindahl,Robert B. O'Hara,Maarja Öpik,Mitchell L. Sogin,Martin Unterseher,Leho Tedersoo +12 more
TL;DR: The aim of the review is to critically discuss the advantages and disadvantages of established and emerging statistical methods, and to contribute to the integration of HTS-based marker gene data into community ecology.
Posted ContentDOI
Rarefaction, alpha diversity, and statistics
TL;DR: It is argued that by failing to account for both bias and variance, analysis of alpha diversity is invalidated, and microbial ecologists are encouraged to avoid motivating their investigations with alpha diversity analyses that do not use valid statistical methodology.
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