sigFeature
sigFeature: Significant feature selection using SVM-RFE & t-statistic
Bioconductor version: 3.23 · Package version: 1.30.0
Other Bioconductor versions
devel is the development version; release is the current stable one.
3.24 (devel), 3.23 (release)
This package provides a novel feature selection algorithm for binary classification using support vector machine recursive feature elimination SVM-RFE and t-statistic. In this feature selection process, the selected features are differentially significant between the two classes and also they are good classifier with higher degree of classification accuracy.
Installation
if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("sigFeature")Details
| Maintainer | Pijush Das Developer <topijush@gmail.com> |
| Author | Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb] |
| License | GPL (>= 2) |
| Source branch | RELEASE_3_23 |
| Build report | Bioconductor build system, r-universe |
| biocViews | Classification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray |
| Package Short Url | https://bioconductor.org/packages/sigFeature/ |
Citation
From within R, enter citation("sigFeature"):
Pijush Das Developer. sigFeature: sigFeature: Significant feature selection using SVM-RFE & t-statistic. doi:10.18129/B9.bioc.sigFeature, R package version 1.30.0, https://bioconductor.org/packages/sigFeature.
Generated from the package metadata; it may differ from the package's own citation.
Documentation
Download
Follow the installation instructions to use this package in your R session.
| Source package | sigFeature_1.30.0.tar.gz |
| Windows binary (x86_64) | sigFeature_1.30.0.zip |
| macOS binary (arm64) | sigFeature_1.30.0.tgz |
| macOS binary (x86_64) | sigFeature_1.30.0.tgz |
Dependencies
Depends: R (>= 3.5.0)
Imports: biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods
Suggests: RUnit, BiocGenerics, knitr, rmarkdown