Non-negative matrix factorization for the analysis of complex gene expression data: identification of clinically relevant tumor subtypes.

ABSTRACT: Non-negative matrix factorization (NMF) is a relatively new approach to analyze gene expression data that models data by additive combinations of non-negative basis vectors (metagenes). The non-negativity constraint makes sense biologically as genes may either be expressed or not, but never show negative expression. We applied NMF to five different microarray data sets. We estimated the appropriate number metagens by comparing the residual error of NMF reconstruction of data to that of NMF reconstruction of permutated data, thus finding when a given solution contained more information than noise. This analysis also revealed that NMF could not factorize one of the data sets in a meaningful way. We used GO categories and pre defined gene sets to evaluate the biological significance of the ob

SUBMITTER: Frigyesi A 

PROVIDER: S-EPMC2623306 | biostudies-literature | 2008

REPOSITORIES: biostudies-literature