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This is the development version of sigFeature; for the stable release version, see sigFeature.

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

Bioconductor version: Development (3.20)

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>

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


To install this package, start R (version "4.4") and enter:

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

# The following initializes usage of Bioc devel


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 1.23.0
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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Source Package sigFeature_1.23.0.tar.gz
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macOS Binary (x86_64) sigFeature_1.23.0.tgz
macOS Binary (arm64) sigFeature_1.23.0.tgz
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