Coclustering of Human Cancer Microarrays Using Minimum Sum-Squared Residue Coclustering

It is a consensus in microarray analysis that identifying potential local patterns, characterized by coherent groups of genes and conditions, may shed light on the discovery of previously undetectable biological cellular processes of genes, as well as macroscopic phenotypes of related samples. In order to simultaneously cluster genes and conditions, we have previously developed a fast coclustering algorithm, Minimum Sum-Squared Residue Coclustering (MSSRCC), which employs an alternating minimization scheme and generates what we call coclusters in a "checkerboard" structure. In this paper, we propose specific strategies that enable MSSRCC to escape poor local minima and resolve the degeneracy problem in partitional clustering algorithms. The strategies include binormalization, deterministic spectral initialization, and incremental local search. We assess the effects of various strategies on both synthetic gene expression data sets and real human cancer microarrays and provide ...

• Type: Journal
• Year: 2008
• Where: TCBB
• Authors: Hyuk Cho, Inderjit S. Dhillon
• Keywords: Potential Local Patterns, Strategies, Sum-Squared Residue Coclustering, TCBB 2008