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# 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) ```r if (!requireNamespace("BiocManager", quietly=TRUE)) install.packages("BiocManager") BiocManager::install("consICA") ``` Package is also available on github ```r library(devtools) install_github("biomod-bsu/consICA") ``` ### Quick start Read vignette ```r browseVignettes("consICA") ``` ### Contact ### References 1. 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).