title: Gene clustering based on clusterwide mutual information.
abstract: Cluster analysis of gene-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and constructing gene regulatory networks. The motivation for considering mutual information is its capacity to measure a general dependence among gene random variables. We propose a novel clustering strategy based on minimizing mutual information among gene clusters. Simulated annealing is employed to solve the optimization problem. Bootstrap techniques are employed to get more accurate estimates of mutual information when the data sample size is small. Moreover, we propose to combine the mutual information criterion and traditional distance criteria such as the Euclidean distance and the fuzzy membership metric in designing the clustering algorithm. The performances of the new clustering methods are compared with those of some existing methods, using both synthesized data and experimental data. It is seen that the clustering algorithm based on a combined metric of mutual information and fuzzy membership achieves the best performance. The supplemental material is available at www.gspsnap.tamu.edu/gspweb/zxb/glioma_zxb.
authors.affiliation: Department of Electrical Engineering, Texas A&M University, College Station, TX 77843, USA.
authors.forename: Xiaobo
authors.initials: X
authors.lastname: Zhou
authors.forename: Xiaodong
authors.initials: X
authors.lastname: Wang
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
authors.forename: Daniel
authors.initials: D
authors.lastname: Russ
authors.forename: Edward
authors.initials: E
authors.lastname: Suh
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated