title: GeneMCL in microarray analysis.
abstract: Accurately and reliably identifying the actual number of clusters present with a dataset of gene expression profiles, when no additional information on cluster structure is available, is a problem addressed by few algorithms. GeneMCL transforms microarray analysis data into a graph consisting of nodes connected by edges, where the nodes represent genes, and the edges represent the similarity in expression of those genes, as given by a proximity measurement. This measurement is taken to be the Pearson correlation coefficient combined with a local non-linear rescaling step. The resulting graph is input to the Markov Cluster (MCL) algorithm, which is an elegant, deterministic, non-specific and scalable method, which models stochastic flow through the graph. The algorithm is inherently affected by any cluster structure present, and rapidly decomposes a graph into cohesive clusters. The potential of the GeneMCL algorithm is demonstrated with a 5,730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms.
authors.affiliation: School of Animal and Microbial Sciences, University of Reading, Whiteknights, Reading RG6 6AJ, UK.
authors.forename: B
authors.initials: B
authors.lastname: Samuel Lattimore
authors.forename: Stijn
authors.initials: S
authors.lastname: van Dongen
authors.forename: M James C
authors.initials: MJ
authors.lastname: Crabbe
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000737:chemistry / Q000235:genetics; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D008390:Markov Chains; D046228:Microarray Analysis / Q000379:methods / Q000706:statistics & numerical data*; D005810:Multigene Family