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
Citations
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Correlation detection strategies in microbial data sets vary widely in sensitivity and precision
Sophie Weiss,Will Van Treuren,Catherine A. Lozupone,Karoline Faust,Karoline Faust,Jonathan Friedman,Ye Deng,Ye Deng,Li C. Xia,Li C. Xia,Zhenjiang Zech Xu,Luke K. Ursell,Eric J. Alm,Amanda Birmingham,Jacob A. Cram,Jed A. Fuhrman,Jeroen Raes,Jeroen Raes,Fengzhu Sun,Jizhong Zhou,Jizhong Zhou,Jizhong Zhou,Rob Knight +22 more
TL;DR: This work benchmarks the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts.
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Intestinal microbiome is related to lifetime antibiotic use in Finnish pre-school children
Katri Korpela,Anne Salonen,Lauri J. Virta,Riina A. Kekkonen,Kristoffer Forslund,Peer Bork,Willem M. de Vos,Willem M. de Vos +7 more
TL;DR: The results support the idea that the impact on the intestinal microbiota should be considered when prescribing antibiotics, and show that macrolide use in 2–7 year-old Finnish children is associated with a long-lasting shift in microbiota composition and metabolism.
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Mycobiome diversity: high-throughput sequencing and identification of fungi.
R. Henrik Nilsson,Sten Anslan,Mohammad Bahram,Christian Wurzbacher,Petr Baldrian,Leho Tedersoo +5 more
TL;DR: An overview and practical recommendations for aspects of HTS studies ranging from sampling and laboratory practices to data processing and analysis are provided, and advice for leveraging next-generation technologies to explore mycobiome diversity in different habitats is provided.
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Microbiome Helper: a Custom and Streamlined Workflow for Microbiome Research.
TL;DR: A streamlined and custom approach to processing samples from detailed sequencing library construction to step-by-step bioinformatic standard operating procedures allows for rapid and reliable microbiome analysis, allowing researchers to focus more on their experiment design and results.
Journal ArticleDOI
Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates
Tobias Guldberg Frøslev,Rasmus Kjøller,Hans Henrik Bruun,Rasmus Ejrnæs,Ane Kirstine Brunbjerg,Carlotta Pietroni,Anders J. Hansen +6 more
TL;DR: A new post-clusturing curation algorithm using OTU co-occurrences to estimate plant biodiversity from soil samples using data derived by high-throughput sequencing of amplified marker genes is presented.
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