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R packages

R is a software environment and programming language for statistical computing and graphics meaning it is applied to process, analyze and plot data. It is free and open source and R packages can be published by every user to provide R functions, compiled code and sample data to others. The following R package are provided by the Seifert group:

RFSurrogates

This R package provides functions to select relevant variables with mutual impurity reduction (MIR) and to analyze variable relationships with mutual forest impact (MFI). These are further developments of the methods from the SurrogateMinimalDepth package.


Github: https://github.com/AGSeifert/RFSurrogates
Publication: Lucas F. Voges, Lukas C. Jarren, Stephan Seifert Bioinformatics 39, btad471 (2023). 

Surrogate Minimal Depth

In this R-package functions are provided to select important variables with minimal depth (MD) and surrogate minimal depth (SMD). In addition, variable relations can be investigated by the mean adjusted agreement of surrogate variables. It has been further developed since 2023 under the name RFSurrogates

Github: https://github.com/StephanSeifert/RFSurrogates
Publication: Stephan Seifert, Sven Gundlach, Silke Szymczak Bioinformatics 35, 3663-3671 (2019).

DoubleqpcR

This R package provides and accompanies the following study. It provides funktions, methods und examples for Double-qpcR experiment (regression) analysis.

Github: https://github.com/LucasFVoges/DoubleqpcR
Publication: Nils Wax, Lucas F. Voges, Soeren H. Wenck, Jana L. Herold, Stephan Seifert, Markus Fischer Food Control 152 (2023).

Pathway Guided Random Forest

This R package provides functions for the identification of important pathways or variable groups using multiple pathway guided random forest (RF) approaches. Furthermore, it includes functions to simulate pathway based gene expression data under two different scenarios.

Github: https://github.com/szymczak-lab/PathwayGuidedRF
Publication: Stephan Seifert, Sven Gundlach, Olaf Junge, Silke Szymczak Bioinformatics 36, 4301-4308 (2020)

DataPathwayGuidedRF

This R package provides the experimental benchmark data sets for the Pathway Guided Random Forest approaches.

Github: https://github.com/szymczak-lab/DataPathwayGuidedRF
Publication: Stephan Seifert, Sven Gundlach, Olaf Junge, Silke Szymczak Bioinformatics 36, 4301-4308 (2020)

Pomona

In this R package various random forest based variable selection approaches like Boruta are reimplemented and provided.

Github: https://github.com/silkeszy/Pomona
Publication: Frauke Degenhardt, Stephan Seifert, Silke Szymczak Briefings in Bioinformatics 20, 492-503 (2019).