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.
journal_name
Comput Biol Chemjournal_title
Computational biology and chemistryauthors
Samuel Lattimore B,van Dongen S,Crabbe MJdoi
10.1016/j.compbiolchem.2005.07.002keywords:
subject
Has Abstractpub_date
2005-10-01 00:00:00pages
354-9issue
5eissn
1476-9271issn
1476-928Xpii
S1476-9271(05)00075-7journal_volume
29pub_type
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