DOI: 10.18129/B9.bioc.consICA    

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

consensus Independent Component Analysis

Bioconductor version: Development (3.17)

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 "4.3") 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.


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HTML R Script The consICA package: User’s manual
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biocViews Classification, FeatureExtraction, RNASeq, Sequencing, Software, StatisticalMethod, Technology, Transcriptomics
Version 1.1.0
In Bioconductor since BioC 3.16 (R-4.2) (< 6 months)
License MIT + file LICENSE
Depends R (>= 4.2.0)
Imports fastICA (>= 1.2.1), sm,, GO.db, stats, SummarizedExperiment, BiocParallel, graph, methods, pheatmap, survival, topGO, graphics, grDevices
Suggests knitr, BiocStyle, rmarkdown, testthat
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