Biclustering via optimal re-ordering of data matrices in systems biology: rigorous methods and comparative studies

Background: The analysis of large-scale data sets via clustering techniques is utilized in a number of applications. Biclustering in particular has emerged as an important problem in the analysis of gene expression data since genes may only jointly respond over a subset of conditions. Biclustering algorithms also have important applications in sample classification where, for instance, tissue samples can be classified as cancerous or normal. Many of the methods for biclustering, and clustering algorithms in general, utilize simplified models or heuristic strategies for identifying the "best" grouping of elements according to some metric and cluster definition and thus result in suboptimal clusters. Results: In this article, we present a rigorous approach to biclustering, OREO, which is based on the Optimal RE-Ordering of the rows and columns of a data matrix so as to globally minimize the dissimilarity metric. The physical permutations of the rows and columns of the data mat...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Peter A. DiMaggio Jr., Scott R. McAllister, Christodoulos A. Floudas, Xiao-Jiang Feng, Joshua D. Rabinowitz, Herschel Rabitz
• Keywords: Biclustering, BMCBI 2008, Cancer Data, Gene Expression Data