title: Conditional clustering of temporal expression profiles.
abstract: BACKGROUND
  Many microarray experiments produce temporal profiles in different biological conditions but common cluster techniques are not able to analyze the data conditional on the biological conditions.
  
  
  RESULTS
  This article presents a novel technique to cluster data from time course microarray experiments performed across several experimental conditions. Our algorithm uses polynomial models to describe the gene expression patterns over time, a full Bayesian approach with proper conjugate priors to make the algorithm invariant to linear transformations, and an iterative procedure to identify genes that have a common temporal expression profile across two or more experimental conditions, and genes that have a unique temporal profile in a specific condition.
  
  
  CONCLUSIONS
  We use simulated data to evaluate the effectiveness of this new algorithm in finding the correct number of clusters and in identifying genes with common and unique profiles. We also use the algorithm to characterize the response of human T cells to stimulations of antigen-receptor signaling gene expression temporal profiles measured in six different biological conditions and we identify common and unique genes. These studies suggest that the methodology proposed here is useful in identifying and distinguishing uniquely stimulated genes from commonly stimulated genes in response to variable stimuli. Software for using this clustering method is available from the project home page.
authors.affiliation: Novartis Vaccines and Diagnostics, Emeryville, CA 94608, USA. ling-1.wang@novartis.com
authors.forename: Ling
authors.initials: L
authors.lastname: Wang
authors.forename: Monty
authors.initials: M
authors.lastname: Montano
authors.forename: Matt
authors.initials: M
authors.lastname: Rarick
authors.forename: Paola
authors.initials: P
authors.lastname: Sebastiani
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D008954:Models, Biological; D005810:Multigene Family / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D020543:Proteome / Q000378:metabolism*; D015398:Signal Transduction / Q000502:physiology*
chemical_list: D020543:Proteome