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GeneMCL in microarray analysis

Lattimore, B. S., van Dongen, S. and Crabbe, M. J. C. (2005) GeneMCL in microarray analysis. Computational Biology and Chemistry, 29 (5). pp. 354-359. ISSN 1476-9271

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To link to this item DOI: 10.1016/j.compbiolchem.2005.07.002


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 5730 gene subset (IGS) of the Van't Veer breast cancer database, for which the clusterings are shown to reflect underlying biological mechanisms. (c) 2005 Elsevier Ltd. All rights reserved.

Item Type:Article
Divisions:Life Sciences > School of Biological Sciences
ID Code:10348
Uncontrolled Keywords:MCL, microarrays, breast cancer, BREAST

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