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Sompairac N, Nazarov PV, 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:E4414. [PMID: 31500324 PMCID: PMC6771121 DOI: 10.3390/ijms20184414] [Citation(s) in RCA: 44] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2019] [Revised: 09/02/2019] [Accepted: 09/04/2019] [Indexed: 12/13/2022] Open
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.
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Affiliation(s)
- Nicolas Sompairac
- Institut Curie, PSL Research University, 75005 Paris, France.
- INSERM U900, 75248 Paris, France.
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France.
- Centre de Recherches Interdisciplinaires, Université Paris Descartes, 75004 Paris, France.
| | - Petr V Nazarov
- Multiomics Data Science Research Group, Quantitative Biology Unit, Luxembourg Institute of Health (LIH), L-1445 Strassen, Luxembourg.
| | - Urszula Czerwinska
- Institut Curie, PSL Research University, 75005 Paris, France.
- INSERM U900, 75248 Paris, France.
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France.
| | - Laura Cantini
- 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.
| | - Anne Biton
- Centre de Bioinformatique, Biostatistique et Biologie Intégrative (C3BI, USR 3756 Institut Pasteur et CNRS), 75015 Paris, France.
| | - Askhat Molkenov
- Laboratory of Bioinformatics and Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan.
| | - Zhaxybay Zhumadilov
- Laboratory of Bioinformatics and Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan.
- University Medical Center, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan.
| | - Emmanuel Barillot
- Institut Curie, PSL Research University, 75005 Paris, France.
- INSERM U900, 75248 Paris, France.
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France.
| | - Francois Radvanyi
- Institut Curie, PSL Research University, 75005 Paris, France.
- CNRS, UMR 144, 75248 Paris, France.
| | - Alexander Gorban
- Center for Mathematical Modeling, University of Leicester, Leicester LE1 7RH, UK.
- Lobachevsky University, 603022 Nizhny Novgorod, Russia.
| | - Ulykbek Kairov
- Laboratory of Bioinformatics and Systems Biology, Center for Life Sciences, National Laboratory Astana, Nazarbayev University, 010000 Nur-Sultan, Kazakhstan.
| | - Andrei Zinovyev
- Institut Curie, PSL Research University, 75005 Paris, France.
- INSERM U900, 75248 Paris, France.
- CBIO-Centre for Computational Biology, Mines ParisTech, PSL Research University, 75006 Paris, France.
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