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Two blind source separation methods (Independent Component Analysis andNon-negative Matrix Factorization), developed initially for signal processingin engineering, found recently a number of applications in analysis oflarge-scale data in molecular biology. In this short review, we present thecommon idea behind these methods, describe ways of implementing and applyingthem and point out to the advantages compared to more traditional statisticalapproaches. We focus more specifically on the analysis of gene expression incancer. The review is finalized by listing available software implementationsfor the methods described.

Blind source separation methods for deconvolution of complex signals in cancer biology