  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.
