Work

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

Year: 2021

Type: article

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.

Source: Applied Sciences

DOI: 10.3390/app112311473

Authors: Vitor Heidrich, Petr Karlovsky, Lukas Beule

Institutions: Universidade de São Paulo, Hospital Sírio-Libanês, University of Göttingen, Julius Kühn-Institut

Language: English

Topic: Microbial Community Ecology and Physiology

Subfield: Ecology

Field: Environmental Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,160

Funders: Fundação de Amparo à Pesquisa do Estado de São Paulo, Bundesministerium für Bildung und Forschung