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.
| Original language | English |
|---|---|
| Pages (from-to) | 1182-1187 |
| Number of pages | 6 |
| Journal | Biochemical and Biophysical Research Communications |
| Volume | 430 |
| Issue number | 3 |
| DOIs | |
| Publication status | Published - Jan 18 2013 |
| Externally published | Yes |
Keywords
- Cancer
- Data analysis
- Gene expression
- Independent component analysis
- Linear data approximation
- Non-negative matrix factorization
ASJC Scopus subject areas
- Biophysics
- Biochemistry
- Molecular Biology
- Cell Biology
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Cite this
Zinovyev, A., Kairov, U., Karpenyuk, T., & Ramanculov, E. (2013). Blind source separation methods for deconvolution of complex signals in cancer biology. Biochemical and Biophysical Research Communications, 430(3), 1182-1187. https://doi.org/10.1016/j.bbrc.2012.12.043