The workflow is a versatile R package designed for comprehensive feature selection in bulk RNAseq datasets. Its key innovation lies in the seamless integration of the 'Python' 'scikit-learn' (<https://scikit-learn.org/stable/index.html>) machine learning framework with R-based bioinformatics tools. 'GeneSelectR' performs robust Machine Learning-driven (ML) feature selection while leveraging 'Gene Ontology' (GO) enrichment analysis as described by Thomas PD et al. (2022) <doi:10.1002/pro.4218>, using 'clusterProfiler' (Wu et al., 2021) <doi:10.1016/j.xinn.2021.100141> and semantic similarity analysis powered by 'simplifyEnrichment' (Gu, Huebschmann, 2021) <doi:10.1016/j.gpb.2022.04.008>. This combination of methodologies optimizes computational and biological insights for analyzing complex RNAseq datasets.
Version: 1.0.1 Depends: R (≥ 3.5.0) Imports: cowplot (≥ 1.1.1), dplyr (≥ 1.1.0), ggplot2 (≥ 3.4.2), glue (≥ 1.6.2), magrittr (≥ 2.0.3), methods (≥ 4.2.2), RColorBrewer (≥ 1.1.3), reshape2 (≥ 1.4.4), reticulate (≥ 1.28), rlang (≥ 1.1.1), testthat (≥ 3.0.0), tibble (≥ 3.2.1), tidyr (≥ 1.3.0), tmod (≥ 0.50.13) Suggests: clusterProfiler (≥ 4.6.2), GO.db (≥ 3.17.0), knitr, rmarkdown, BiocManager (≥ 1.30.21), UpSetR (≥ 1.4.0), AnnotationHub (≥ 3.8.0), ensembldb (≥ 2.24.0), org.Hs.eg.db (≥ 3.17.0) Enhances: simplifyEnrichment (≥ 1.8.0) Published: 2024-02-03 DOI: 10.32614/CRAN.package.GeneSelectR Author: Damir Zhakparov [aut, cre] Maintainer: Damir Zhakparov <dzhakparov at gmail.com> BugReports: https://github.com/dzhakparov/GeneSelectR/issues License: MIT + file LICENSE URL: https://github.com/dzhakparov/GeneSelectR NeedsCompilation: no Materials: README CRAN checks: GeneSelectR resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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