DataVXApplied SciencesPaper
journal article·2021-12-03Open access

‘SRS’ R Package and ‘q2-srs’ QIIME 2 Plugin: Normalization of Microbiome Data Using Scaling with Ranked Subsampling (SRS)

Vitor Heidrich, Petr Karlovsky, Lukas Beule

Applied Sciences· Volume 11· Issue 23
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Source abstract

Abstract

Several ecological data types, especially microbiome count data, are commonly sample-wise normalized before analysis to correct for sampling bias and other technical artifacts. Recently, we developed an algorithm for the normalization of ecological count data called ‘scaling with ranked subsampling (SRS)’, which surpasses the widely adopted ‘rarefying’ (random subsampling without replacement) in reproducibility and in safeguarding the original community structure. Here, we describe an implementation of the SRS algorithm in the ‘SRS’ R package and the ‘q2-srs’ QIIME 2 plugin. We also provide accessory functions for dataset exploration to guide the choice of parameters for SRS.
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Research context

OpenAlex topics: Gut microbiota and health · Microbial Community Ecology and Physiology · Bacterial Identification and Susceptibility Testing

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Affiliations

Centro de Oncologia Molecular, Hospital Sírio-Libanês, São Paulo 01308-060, Brazil

Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, São Paulo 05508-900, Brazil

Molecular Phytopathology and Mycotoxin Research, Faculty of Agricultural Sciences, University of Goettingen, 37077 Goettingen, Germany

Julius Kühn Institute (JKI)—Federal Research Centre for Cultivated Plants, Institute for Ecological Chemistry, Plant Analysis and Stored Product Protection, 14195 Berlin, Germany

Funding

Federal Ministry of Education and Research · Fundação de Amparo à Pesquisa do Estado de São Paulo

References (13)

Show source-provided references
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