Biclustering of gene expression data by non-smooth non-negative matrix factorization

Background: The extended use of microarray technologies has enabled the generation and accumulation of gene expression datasets that contain expression levels of thousands of genes across tens or hundreds of different experimental conditions. One of the major challenges in the analysis of such datasets is to discover local structures composed by sets of genes that show coherent expression patterns across subsets of experimental conditions. These patterns may provide clues about the main biological processes associated to different physiological states. Results: In this work we present a methodology able to cluster genes and conditions highly related in sub-portions of the data. Our approach is based on a new data mining technique, Non-smooth Non-Negative Matrix Factorization (nsNMF), able to identify localized patterns in large datasets. We assessed the potential of this methodology analyzing several synthetic datasets as well as two large and heterogeneous sets of gene expression pro...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Pedro Carmona-Saez, Roberto D. Pascual-Marqui, Francisco Tirado, José María Carazo, Alberto D. Pascual-Montano
• Keywords: BMCBI 2006, Expression Level, Expression Patterns, Gene Expression Datasets