Dataset Information

ConsICA: an R package for robust reference-free deconvolution of multi-omics data.

ABSTRACT:

Motivation

Deciphering molecular signals from omics data helps understanding cellular processes and disease progression. Effective algorithms for extracting these signals are essential, with a strong emphasis on robustness and reproducibility.

Results

R/Bioconductor package consICA implements consensus independent component analysis (ICA)-a data-driven deconvolution method to decompose heterogeneous omics data and extract features suitable for patient stratification and multimodal data integration. The method separates biologically relevant molecular signals from technical effects and provides information about the cellular composition and biological processes. Build-in annotation, survival analysis, and report generation provide useful tools for the interpretation of extracted signals. The implementation of parallel computing in the package ensures efficient analysis using modern multicore systems. The package offers a reproducible and efficient data-driven solution for the analysis of complex molecular profiles, with significant implications for cancer research.

Availability and implementation

The package is implemented in R and available under MIT license at Bioconductor (https://bioconductor.org/packages/consICA) or at GitHub (https://github.com/biomod-lih/consICA).

SUBMITTER: Chepeleva M

PROVIDER: S-EPMC11257712 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature

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Publications

consICA: an R package for robust reference-free deconvolution of multi-omics data.

Chepeleva Maryna M Kaoma Tony T Zinovyev Andrei A Toth Reka R Nazarov Petr V PV

Bioinformatics advances 20240713 1

<h4>Motivation</h4>Deciphering molecular signals from omics data helps understanding cellular processes and disease progression. Effective algorithms for extracting these signals are essential, with a strong emphasis on robustness and reproducibility.<h4>Results</h4>R/Bioconductor package <i>consICA</i> implements consensus independent component analysis (ICA)-a data-driven deconvolution method to decompose heterogeneous omics data and extract features suitable for patient stratification and mul ...[more]

PMID: 39027644

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