title: Improving molecular cancer class discovery through sparse non-negative matrix factorization.
abstract: BACKGROUND
  Identifying different cancer classes or subclasses with similar morphological appearances presents a challenging problem and has important implication in cancer diagnosis and treatment. Clustering based on gene-expression data has been shown to be a powerful method in cancer class discovery. Non-negative matrix factorization is one such method and was shown to be advantageous over other clustering techniques, such as hierarchical clustering or self-organizing maps. In this paper, we investigate the benefit of explicitly enforcing sparseness in the factorization process.
  
  
  RESULTS
  We report an improved unsupervised method for cancer classification by the use of gene-expression profile via sparse non-negative matrix factorization. We demonstrate the improvement by direct comparison with classic non-negative matrix factorization on the three well-studied datasets. In addition, we illustrate how to identify a small subset of co-expressed genes that may be directly involved in cancer.
authors.affiliation: Department of Genetics, Harvard Medical School Boston, MA 02115, USA. g1m1c1@receptor.med.harvard.edu
authors.forename: Yuan
authors.initials: Y
authors.lastname: Gao
authors.forename: George
authors.initials: G
authors.lastname: Church
mesh_terms: D000465:Algorithms; D014408:Biomarkers, Tumor / Q000145:classification / Q000378:metabolism*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D005163:Factor Analysis, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D009363:Neoplasm Proteins / Q000145:classification / Q000378:metabolism*; D009369:Neoplasms / Q000145:classification / Q000175:diagnosis* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins