Metagenes and molecular pattern discovery using matrix factorization

We describe here the use of nonnegative matrix factorization (NMF), an algorithm based on decomposition by parts that can reduce the dimension of expression data from thousands of genes to a handful of metagenes. Coupled with a model selection mechanism, adapted to work for any stochastic clustering algorithm, NMF is an efficient method for identification of distinct molecular patterns and provides a powerful method for class discovery. We demonstrate the ability of NMF to recover meaningful biological information from cancer-related microarray data. NMF appears to have advantages over other methods such as hierarchical clustering or self-organizing maps. We found it less sensitive to a priori selection of…

• A
  AstraZeneca
• MI
  Massachusetts Institute of Technology
• HU
  Harvard University
• BI
  Broad Institute
• NI
  National Institutes of Health