Blind source separation methods for deconvolution of complex signals in cancer biology |
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| Authors: | Andrei Zinovyev Ulykbek Kairov Tatyana Karpenyuk Erlan Ramanculov |
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| Affiliation: | 1. Institute Curie, Paris, France;2. INSERM U900, Paris, France;3. Mines ParisTech, Fontainebleau, France;4. Kazakh National University after Al-Farabi, Almaty, Kazakhstan;5. National Center for Biotechnology of the Republic of Kazakhstan, Astana, Kazakhstan;1. Bar-Ilan University, Faculty of Engineering, 5290002 Ramat-Gan, Israel;2. KU Leuven, Department of Electrical Engineering (ESAT), Stadius Center for Dynamical Systems, Signal Processing and Data Analytics, Kasteelpark Arenberg 10, 3001 Leuven, Belgium;3. Complex Carbohydrate Research Center,;4. Institute of Bioinformatics,;5. Department of Statistics, University of Georgia, Athens, GA, 30602;6. Department of BioMolecular Sciences, University of Mississippi, University, MS, 38677;1. Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan;2. Department of Computer Science and Engineering, Tatung University, Taipei, 10451, Taiwan;3. Faculty of Technology, Art and Design, Oslo and Akershus University College of Applied Sciences, Oslo, Norway;4. Faculty of Technology, Westerdals School of Art, Communication and Technology, Oslo, Norway;1. Icahn School of Medicine at Mount Sinai, Department of Urology, 1 Gustave L. Levy Pl, New York, NY 10029, USA;2. Icahn School of Medicine at Mount Sinai, Department of Medical Oncology, 1 Gustave L. Levy Pl, New York, NY 10029, USA;1. Department of Medical Oncology, Campus Bio-Medico University of Rome, Rome, Italy;2. INSERM, Research Unit U1033, University of Lyon-1, Faculty of Medicine Laennec, rue Guillaume Paradin, 69372 Lyon cedex 08, France;3. INSERM, Research Unit U1033, University of Lyon-1, Faculty of Medicine Laennec, rue Guillaume Paradin, 69372 Lyon cedex 08, France;4. Department of Medical Oncology, Campus Bio-Medico University of Rome, Rome, Italy |
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| Abstract: | Two blind source separation methods (Independent Component Analysis and Non-negative Matrix Factorization), developed initially for signal processing in engineering, found recently a number of applications in analysis of large-scale data in molecular biology. In this short review, we present the common idea behind these methods, describe ways of implementing and applying them and point out to the advantages compared to more traditional statistical approaches. We focus more specifically on the analysis of gene expression in cancer. The review is finalized by listing available software implementations for the methods described. |
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