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Keywords {🔍}

data, samples, pubmed, article, normalization, google, scholar, abundance, methods, library, size, analysis, sample, differential, rarefying, microbial, sizes, additional, sequencing, fig, deseq, central, microbiome, otus, statistical, relative, cas, groups, results, techniques, testing, file, simulation, counts, distribution, multinomial, depth, compositional, false, model, unifrac, fdr, taxa, taxon, test, figure, metrics, ancom, true, tests,

Topics {✒️}

mann-whitney/wilcoxon rank-sum test zhenjiang zech xu microbial marker-gene surveys indo-pacific coral reefs article download pdf subsampling-based normalization strategies estimators linking individual-based wilcoxon rank-sum test high-throughput sequencing datasets apply high-throughput sequencing differential abundance-testing methods sparse high-throughput sequencing low-income countries leads yoshiki vázquez-baeza face difficult trade-offs rna-seq read counts nonparametric mann-whitney test log-ratio approaches inspired observed substantial biases/confounding gamma-poisson results converged differential abundance analyses wilcoxon rank-sum dirichlet-multinomial results converged removing low-depth samples presence/absence distance metrics privacy choices/manage cookies differential abundance testing high-throughput sequencing related subjects significantly differentially abundant caporaso jg x-axis effect size simulating differential abundance optimal test-statistics [79] differential expression analysis intramural research program 16s ribosomal rna differential abundance analysis proportion-normalized data outperforms reflecting differential efficiency creative commons license differential abundance methods sequencing detection ability bacterial community variation variance-stabilizing transformation performed human gut microbiome 16s rrna diversity sparse dirichlet-multinomial regression bray-curtis [59] dissimilarity open software development

Questions {❓}

• PERMANOVA, ANOSIM, and the Mantel test in the face of heterogeneous dispersions: what null hypothesis are you testing?

Schema {🗺️}

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         headline:Normalization and microbial differential abundance strategies depend upon data characteristics
         description:Data from 16S ribosomal RNA (rRNA) amplicon sequencing present challenges to ecological and statistical interpretation. In particular, library sizes often vary over several ranges of magnitude, and the data contains many zeros. Although we are typically interested in comparing relative abundance of taxa in the ecosystem of two or more groups, we can only measure the taxon relative abundance in specimens obtained from the ecosystems. Because the comparison of taxon relative abundance in the specimen is not equivalent to the comparison of taxon relative abundance in the ecosystems, this presents a special challenge. Second, because the relative abundance of taxa in the specimen (as well as in the ecosystem) sum to 1, these are compositional data. Because the compositional data are constrained by the simplex (sum to 1) and are not unconstrained in the Euclidean space, many standard methods of analysis are not applicable. Here, we evaluate how these challenges impact the performance of existing normalization methods and differential abundance analyses. Effects on normalization: Most normalization methods enable successful clustering of samples according to biological origin when the groups differ substantially in their overall microbial composition. Rarefying more clearly clusters samples according to biological origin than other normalization techniques do for ordination metrics based on presence or absence. Alternate normalization measures are potentially vulnerable to artifacts due to library size. Effects on differential abundance testing: We build on a previous work to evaluate seven proposed statistical methods using rarefied as well as raw data. Our simulation studies suggest that the false discovery rates of many differential abundance-testing methods are not increased by rarefying itself, although of course rarefying results in a loss of sensitivity due to elimination of a portion of available data. For groups with large (~10×) differences in the average library size, rarefying lowers the false discovery rate. DESeq2, without addition of a constant, increased sensitivity on smaller datasets (<20 samples per group) but tends towards a higher false discovery rate with more samples, very uneven (~10×) library sizes, and/or compositional effects. For drawing inferences regarding taxon abundance in the ecosystem, analysis of composition of microbiomes (ANCOM) is not only very sensitive (for >20 samples per group) but also critically the only method tested that has a good control of false discovery rate. These findings guide which normalization and differential abundance techniques to use based on the data characteristics of a given study.
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         Bioinformatics
         Microbial Ecology
         Microbiology
         Microbial Genetics and Genomics
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      name:Department of Microbiology, University of Pennsylvania, Philadelphia, USA
      name:Departments of Pediatrics, University of California San Diego, La Jolla, USA
      name:Department of Medicine, University of Colorado, Denver, USA
      name:Department of Microbiology, Oregon State University, Corvallis, USA
      name:Department of Computer Science & Engineering, University of California San Diego, La Jolla, USA
      name:Center for Computational Biology and Bioinformatics, Dept. of Medicine, University of California San Diego, La Jolla, USA
      name:Departments of Pediatrics, University of California San Diego, La Jolla, USA
      name:Departments of Pediatrics, University of California San Diego, La Jolla, USA
      name:Department of Computer Science & Engineering, University of California San Diego, La Jolla, USA
      name:Center for Microbiome Innovation, University of California San Diego, La Jolla, USA

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