consICA: Consensus ICA R-package for multiomics data analysis

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 cellular composition and biological processes [1]. The implementation of parallel computing in the package ensures the efficient analysis on the modern multicore systems.

Installation

Package is available from bioconductor 3.15 (R version >= 4.1.0)

if (!requireNamespace("BiocManager", quietly=TRUE))
    install.packages("BiocManager")
BiocManager::install("consICA")

Package is also available on GitHub

library(devtools)
install_github("biomod-lih/consICA")

For Linux users

As for fast data calculation we use the RcppGSl package in dependencies, for Linux it requires a separately installed libgsl0-dev library. Please install it before the consICA installation:

sudo apt-get update -y
sudo apt-get install -y libgsl0-dev

Quick start

Read vignette

browseVignettes("consICA")

Contact

petr.nazarov@lih.lu

References

Maryna Chepeleva, Tony Kaoma, Andrei Zinovyev, Reka Toth, Petr V Nazarov, consICA: an R package for robust reference-free deconvolution of multi-omics data, Bioinformatics Advances, Volume 4, Issue 1, 2024, vbae102, https://doi.org/10.1093/bioadv/vbae102
Nazarov, P.V., Wienecke-Baldacchino, A.K., Zinovyev, A. et al. Deconvolution of transcriptomes and miRNomes by independent component analysis provides insights into biological processes and clinical outcomes of melanoma patients. BMC Med Genomics 12, 132 (2019). https://doi.org/10.1186/s12920-019-0578-4
Chepeleva M, Kaoma T, Muller A et al. сonsICA: Multimodal data deconvolution, integration and elucidation of biological processes in cancer research [version 1; not peer reviewed]. F1000Research 2023, 12:1260 (Poster). https://doi.org/10.7490/f1000research.1119635.1