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Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

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Institut Curie, PSL Research University, 75005 Paris, France
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INSERM U900, 75248 Paris, France
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CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France
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Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75004 Paris, France
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Multiomics Data Science Research Group, Quantitative Biology Unit, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg
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Computational Systems Biology Team, Institut de Biologie de l’Ecole Normale Supérieure, CNRS UMR8197, INSERM U1024, Ecole Normale Supérieure, PSL Research University, 75005 Paris, France
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Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI, USR 3756 Institut Pasteur et CNRS), 75015 Paris, France
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Laboratory of Bioinformatics and Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan
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University Medical Center, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan
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CNRS, UMR 144, 75248 Paris, France
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Center for Mathematical Modeling, University of Leicester, Leicester LE1 7RH, UK
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Lobachevsky University, 603022 Nizhny Novgorod, Russia
*
Author to whom correspondence should be addressed.
Int. J. Mol. Sci. 2019, 20(18), 4414; https://doi.org/10.3390/ijms20184414
Received: 3 August 2019 / Revised: 2 September 2019 / Accepted: 4 September 2019 / Published: 7 September 2019
(This article belongs to the Special Issue Data Analysis and Integration in Cancer Research)
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. View Full-Text
Keywords: independent component analysis; cancer; omics data; dimension reduction; data analysis; data integration independent component analysis; cancer; omics data; dimension reduction; data analysis; data integration
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Sompairac, N.; Nazarov, P.V.; Czerwinska, U.; Cantini, L.; Biton, A.; Molkenov, A.; Zhumadilov, Z.; Barillot, E.; Radvanyi, F.; Gorban, A.; Kairov, U.; Zinovyev, A. Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets. Int. J. Mol. Sci. 2019, 20, 4414.

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