Waste not, want not: why rarefying microbiome data is inadmissible.
Citations
47,038 citations
Cites background from "Waste not, want not: why rarefying ..."
..., [43]), ribosome profiling [44] and CRISPR/Cas-library assays [45]....
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17,014 citations
Cites methods from "Waste not, want not: why rarefying ..."
..., [44]), ribosome profiling [45] and CRISPR/Caslibrary assays [46]....
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2,818 citations
1,673 citations
Cites methods from "Waste not, want not: why rarefying ..."
...This method was chosen due to its sensitivity for detecting differentially abundant taxa compared with traditional microbiome normalization techniques such as rarefaction and relative abundance (20)....
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1,511 citations
Cites background or methods from "Waste not, want not: why rarefying ..."
...This is implicitly acknowledged when microbiome datasets are converted to relative abundance values, or normalized counts, or are rarefied (McMurdie and Holmes, 2014; Weiss et al., 2017) prior to analysis....
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...calculations for multivariate ordinations derived from these distances (McMurdie and Holmes, 2014)....
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...The use of subsampling has been questioned since it results in a loss of information and precision (McMurdie and Holmes, 2014), and the practice of count normalization from the RNAseq field has been advocated instead....
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...The table below in (C) shows real and perceived changes for each sample if we transition from one sample to another. calculations for multivariate ordinations derived from these distances (McMurdie and Holmes, 2014)....
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...Methods applied include count-based strategies such as Bray-Curtis dissimilarity, zero-inflated Gaussianmodels and negative binomial models (McMurdie and Holmes, 2014; Weiss et al., 2017)....
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References
272,030 citations
83,420 citations
"Waste not, want not: why rarefying ..." refers methods in this paper
...All tests were corrected for multiple inferences using the Benjamini-Hochberg method to control the False Discovery Rate [53]....
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...All tests were corrected for multiple inferences using the Benjamini-Hochberg method to control the False Discovery Rate [52]....
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...For all methods, detection among multiple tests was defined using a False Discovery Rate (Benjamini-Hochberg [52]) significance threshold of 0....
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29,504 citations
"Waste not, want not: why rarefying ..." refers background or methods in this paper
...These simulations, analyses, and graphics rely upon the cluster [58], foreach [59], ggplot2 [60], phyloseq [53], plyr [61], reshape2 [62], and ROCR [39] R packages; in addition to the DESeq [3], edgeR [2], and PoiClaClu [63] R packages for RNASeq data, and tools available in the standard R distribution [64]....
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...Hadley Wickham created and continues to support the ggplot2 [60] and reshape [62]/plyr [61] packages that have proven useful for graphical representation and manipulation of data, respectively....
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29,413 citations
"Waste not, want not: why rarefying ..." refers methods in this paper
...This approach is already wellcharacterized and implemented for RNA-Seq data in R packages such as edgeR [2] and DESeq [3]....
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...We would like to thank the developers of the open source packages leveraged here for improved insights into microbiome data, in particular Gordon Smyth and his group for edgeR [2], and Wolfgang Huber and his team for DESeq [3]; whose useful documentation and continued support have been invaluable....
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...We utilize the most popular implementations of this approach currently used in RNA-Seq analysis, namely edgeR [2] and DESeq [3], adapted here for microbiome data....
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...These simulations, analyses, and graphics rely upon the cluster [58], foreach [59], ggplot2 [60], phyloseq [53], plyr [61], reshape2 [62], and ROCR [39] R packages; in addition to the DESeq [3], edgeR [2], and PoiClaClu [63] R packages for RNASeq data, and tools available in the standard R distribution [64]....
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28,911 citations
"Waste not, want not: why rarefying ..." refers background in this paper
...We would also like to thank Rob Knight and his lab for QIIME [26], which has drastically decreased the time required to get from raw phylogenetic sequence data to OTU counts....
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...Rarefying is now an exceedingly common precursor to microbiome multivariate workflows that seek to relate sample covariates to sample-wise distance matrices [19, 27, 28]; for example, integrated as a recommended option in QIIME’s [29] beta_diversity_through_plots.py workflow, in Sub.sample in the mothur software library [30], in daisychopper.pl [31], and is even supported in phyloseq’s rarefy_even_depth function [32] (though not recommended in its documentation)....
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...We would also like to thank Rob Knight and his lab for QIIME [29], which has drastically decreased the time required to get from raw phylogenetic sequence data to OTU counts....
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...Rarefying is now an exceedingly common precursor to microbiome multivariate workflows that seek to relate sample covariates to sample-wise distance matrices [6, 24, 25]; for example, integrated as a recommended option in QIIME’s [26] beta-diversity-through-plots....
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...Some heuristics for filtering low-abundance OTUs are already described in the documentation of various microbiome analysis workflows [26, 27], and in many cases these are a form of Independent Filtering....
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