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      Rhea: a transparent and modular R pipeline for microbial profiling based on 16S rRNA gene amplicons

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          Abstract

          The importance of 16S rRNA gene amplicon profiles for understanding the influence of microbes in a variety of environments coupled with the steep reduction in sequencing costs led to a surge of microbial sequencing projects. The expanding crowd of scientists and clinicians wanting to make use of sequencing datasets can choose among a range of multipurpose software platforms, the use of which can be intimidating for non-expert users. Among available pipeline options for high-throughput 16S rRNA gene analysis, the R programming language and software environment for statistical computing stands out for its power and increased flexibility, and the possibility to adhere to most recent best practices and to adjust to individual project needs. Here we present the Rhea pipeline, a set of R scripts that encode a series of well-documented choices for the downstream analysis of Operational Taxonomic Units (OTUs) tables, including normalization steps, alpha- and beta-diversity analysis, taxonomic composition, statistical comparisons, and calculation of correlations. Rhea is primarily a straightforward starting point for beginners, but can also be a framework for advanced users who can modify and expand the tool. As the community standards evolve, Rhea will adapt to always represent the current state-of-the-art in microbial profiles analysis in the clear and comprehensive way allowed by the R language. Rhea scripts and documentation are freely available at https://lagkouvardos.github.io/Rhea.

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          Most cited references17

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            A mathematical theory of communication

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              Independent filtering increases detection power for high-throughput experiments.

              With high-dimensional data, variable-by-variable statistical testing is often used to select variables whose behavior differs across conditions. Such an approach requires adjustment for multiple testing, which can result in low statistical power. A two-stage approach that first filters variables by a criterion independent of the test statistic, and then only tests variables which pass the filter, can provide higher power. We show that use of some filter/test statistics pairs presented in the literature may, however, lead to loss of type I error control. We describe other pairs which avoid this problem. In an application to microarray data, we found that gene-by-gene filtering by overall variance followed by a t-test increased the number of discoveries by 50%. We also show that this particular statistic pair induces a lower bound on fold-change among the set of discoveries. Independent filtering-using filter/test pairs that are independent under the null hypothesis but correlated under the alternative-is a general approach that can substantially increase the efficiency of experiments.
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                Author and article information

                Contributors
                Journal
                PeerJ
                PeerJ
                peerj
                peerj
                PeerJ
                PeerJ Inc. (San Francisco, USA )
                2167-8359
                11 January 2017
                2017
                : 5
                : e2836
                Affiliations
                [-1]ZIEL—Core Facility Microbiome/NGS, Technical University of Munich , Freising, Germany
                Article
                2836
                10.7717/peerj.2836
                5234437
                28097056
                dddfd61c-e152-4b82-9212-b2876ed2addc
                ©2017 Lagkouvardos et al.

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ) and either DOI or URL of the article must be cited.

                History
                : 29 July 2016
                : 28 November 2016
                Funding
                Funded by: German Research Foundation (DFG)
                Funded by: Technical University of Munich (TUM)
                This work was supported by the German Research Foundation (DFG) and the Technical University of Munich (TUM) in the framework of the Open Access Publishing Program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Bioinformatics
                Ecology
                Microbiology

                16s rrna gene,microbiome analysis,r script,microbial profile,host-microbe interactions,statistical analysis,alpha-diversity,beta-diversity,correlations

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