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sigFeature: Significant feature selection using SVM-RFE & t-statistic

Bioconductor version: Release (3.18)

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.

Author: Pijush Das Developer [aut, cre], Dr. Susanta Roychudhury User [ctb], Dr. Sucheta Tripathy User [ctb]

Maintainer: Pijush Das Developer <topijush at gmail.com>

Citation (from within R, enter citation("sigFeature")):


To install this package, start R (version "4.3") and enter:

if (!require("BiocManager", quietly = TRUE))


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

sigFeature HTML R Script
Reference Manual PDF


biocViews Classification, FeatureExtraction, GeneExpression, GenePrediction, Microarray, Normalization, Software, SupportVectorMachine, Transcription, mRNAMicroarray
Version 1.20.0
In Bioconductor since BioC 3.8 (R-3.5) (5.5 years)
License GPL (>= 2)
Depends R (>= 3.5.0)
Imports biocViews, nlme, e1071, openxlsx, pheatmap, RColorBrewer, Matrix, SparseM, graphics, stats, utils, SummarizedExperiment, BiocParallel, methods
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Suggests RUnit, BiocGenerics, knitr, rmarkdown
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Follow Installation instructions to use this package in your R session.

Source Package sigFeature_1.20.0.tar.gz
Windows Binary sigFeature_1.20.0.zip
macOS Binary (x86_64) sigFeature_1.20.0.tgz
macOS Binary (arm64) sigFeature_1.20.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/sigFeature
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/sigFeature
Bioc Package Browser https://code.bioconductor.org/browse/sigFeature/
Package Short Url https://bioconductor.org/packages/sigFeature/
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