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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Differential effects of selective and non-selective cyclooxygenase inhibitors on fecal microbiota in adult horses
Canaan M. Whitfield-Cargile,Ana M. Chamoun-Emanuelli,Noah D. Cohen,Lauren M. Richardson,Nadim J. Ajami,Hannah J. Dockery +5 more
TL;DR: While the fecal microbiota profile of the control group remained stable over time, the phenylbutazone and firocoxib groups had decreased diversity, and alteration of their microbiota profiles was most pronounced at day 10, indicating that use of both non-selective and selective COX inhibitors resulted in temporary alterations of the stool microbiota and inferred metagenome.
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Latent Variable Modeling for the Microbiome
Kris Sankaran,Susan Holmes +1 more
TL;DR: This paper explored the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet Allocation, Nonnegative Matrix Factorization, and Dynamic Unigram models.
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Leaf Treatments with a Protein-Based Resistance Inducer Partially Modify Phyllosphere Microbial Communities of Grapevine
M. Cappelletti,Michele Perazzolli,Livio Antonielli,Andrea Nesler,Esmeralda Torboli,Pier Luigi Bianchedi,Massimo Pindo,Gerardo Puopolo,Ilaria Pertot +8 more
TL;DR: Modifying phyllosphere populations by increasing natural biocontrol agents with the application of selected nutritional factors can open new opportunities in terms of sustainable plant protection strategies, partially affecting the hormone-mediated signaling pathways involved.
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Extraction and 16S rRNA Sequence Analysis of Microbiomes Associated with Rice Roots
TL;DR: A protocol for dissecting the microbiota from various root compartments using rice as a model is presented and a method for amplifying fragments of the 16S rRNA gene using a dual index approach is presented.
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Microbiomes of gall-inducing copepod crustaceans from the corals Stylophora pistillata (Scleractinia) and Gorgonia ventalina (Alcyonacea).
Pavel V. Shelyakin,Sofya K. Garushyants,Mikhail A. Nikitin,Sofya V. Mudrova,Michael L. Berumen,Arjen G. C. L. Speksnijder,Bert W. Hoeksema,Diego Fontaneto,Mikhail S. Gelfand,Viatcheslav N. Ivanenko,Viatcheslav N. Ivanenko +10 more
TL;DR: The microbial community composition of the injured gall tissues was not directly affected by the microbiome of the gall-forming symbiont copepods, and no bacterial group had significantly different prevalence in the normal coral tissues, copepod, and injured tissues.
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