title: Blind source separation methods for deconvolution of complex signals in cancer biology.
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.
authors.affiliation: Institute Curie, Paris, France. andrei.zinovyev@curie.fr
authors.forename: Andrei
authors.initials: A
authors.lastname: Zinovyev
authors.forename: Ulykbek
authors.initials: U
authors.lastname: Kairov
authors.forename: Tatyana
authors.initials: T
authors.lastname: Karpenyuk
authors.forename: Erlan
authors.initials: E
authors.lastname: Ramanculov
mesh_terms: D014408:Biomarkers, Tumor / Q000235:genetics*; D002471:Cell Transformation, Neoplastic / Q000235:genetics; D020869:Gene Expression Profiling / Q000706:statistics & numerical data*; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D025341:Principal Component Analysis / Q000379:methods*; D059467:Transcriptome
chemical_list: D014408:Biomarkers, Tumor