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consensus Independent Component Analysis

Bioconductor version: Release (3.19)

consICA implements a data-driven deconvolution method – consensus independent component analysis (ICA) to decompose heterogeneous omics data and extract features suitable for patient diagnostics and prognostics. The method separates biologically relevant transcriptional signals from technical effects and provides information about the cellular composition and biological processes. The implementation of parallel computing in the package ensures efficient analysis of modern multicore systems.

Author: Petr V. Nazarov [aut, cre] , Tony Kaoma [aut] , Maryna Chepeleva [aut]

Maintainer: Petr V. Nazarov <petr.nazarov at>

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


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

The consICA package: User’s manual HTML R Script
Reference Manual PDF


biocViews Classification, FeatureExtraction, RNASeq, Sequencing, Software, StatisticalMethod, Technology, Transcriptomics
Version 2.2.0
In Bioconductor since BioC 3.16 (R-4.2) (1.5 years)
License MIT + file LICENSE
Depends R (>= 4.2.0)
Imports fastICA (>= 1.2.1), sm,, GO.db, stats, SummarizedExperiment, BiocParallel, graph, ggplot2, methods, Rfast, pheatmap, survival, topGO, graphics, grDevices
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Suggests knitr, BiocStyle, rmarkdown, testthat, Seurat
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Follow Installation instructions to use this package in your R session.

Source Package consICA_2.2.0.tar.gz
Windows Binary
macOS Binary (x86_64) consICA_2.2.0.tgz
macOS Binary (arm64) consICA_2.2.0.tgz
Source Repository git clone
Source Repository (Developer Access) git clone
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