R package for microbiome count data normalization by scaling with ranked subsampling (SRS)
Read more about this normalization method in the SRS paper (Beule and Karlovsky, PeerJ 2020).
Install the latest version from CRAN by running:
install.packages("SRS")The SRS R package features three R functions:
SRS()- performs SRS normalization at a user-defined number of reads per sample (Cmin)SRScurve()- draws alpha diversity rarefaction curves for SRS-normalized data (instead of rarefied data)SRS.shiny.app()- generates a visualization of retained samples, summary statistics, SRS curves, and an interactive table in response to varying Cmin
Refer to the SRS reference manual for usage details.
If you use this package in your research paper, please cite as:
Heidrich V, Karlovsky P, Beule L. 2021. ‘SRS’ R package and ‘q2-srs’ QIIME 2 plugin: Normalization of Microbiome Data Using Scaling with Ranked Subsampling (SRS). Appl. Sci. 11(23), 11473.
When referencing the SRS algorithm itself, please cite:
Beule L, Karlovsky P. 2020. Improved normalization of species count data in ecology by scaling with ranked subsampling (SRS): application to microbial communities. PeerJ 8:e9593.