Please use this identifier to cite or link to this item: http://hdl.handle.net/2381/46084
Title: Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets.
Authors: Sompairac, N
Nazarov, PV
Czerwinska, U
Cantini, L
Biton, A
Molkenov, A
Zhumadilov, Z
Barillot, E
Radvanyi, F
Gorban, A
Kairov, U
Zinovyev, A
First Published: 7-Sep-2019
Publisher: MDPI
Citation: International Journal of Molecular Sciences, 2019, 20 (18)
Abstract: Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.
DOI Link: 10.3390/ijms20184414
eISSN: 1422-0067
Links: https://www.mdpi.com/1422-0067/20/18/4414
http://hdl.handle.net/2381/46084
Version: Publisher Version
Status: Peer-reviewed
Type: Journal Article
Rights: Copyright © the authors, 2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Appears in Collections:Published Articles, Dept. of Mathematics

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