title: Multi-way clustering of microarray data using probabilistic sparse matrix factorization.
abstract: BACKGROUND
  We address the problem of multi-way clustering of microarray data using a generative model. Our algorithm, probabilistic sparse matrix factorization (PSMF), is a probabilistic extension of a previous hard-decision algorithm for this problem. PSMF allows for varying levels of sensor noise in the data, uncertainty in the hidden prototypes used to explain the data and uncertainty as to the prototypes selected to explain each data vector.
  
  
  RESULTS
  We present experimental results demonstrating that our method can better recover functionally-relevant clusterings in mRNA expression data than standard clustering techniques, including hierarchical agglomerative clustering, and we show that by computing probabilities instead of point estimates, our method avoids converging to poor solutions.
authors.affiliation: Department of Electrical and Computer Engineering, University of Toronto Toronto, Ontario, Canada M5S 3G4. delbert@psi.toronto.edu
authors.forename: Delbert
authors.initials: D
authors.lastname: Dueck
authors.forename: Quaid D
authors.initials: QD
authors.lastname: Morris
authors.forename: Brendan J
authors.initials: BJ
authors.lastname: Frey
mesh_terms: D000465:Algorithms; D000818:Animals; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D016678:Genome; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011336:Probability; D012333:RNA, Messenger / Q000378:metabolism; D012984:Software
chemical_list: D012333:RNA, Messenger