title: Finding dominant sets in microarray data.
abstract: Clustering allows us to extract groups of genes that are tightly coexpressed from Microarray data. In this paper, a new method DSF_Clust is developed to find dominant sets (clusters). We have preformed DSF_Clust on several gene expression datasets and given the evaluation with some criteria. The results showed that this approach could cluster dominant sets of good quality compared to kmeans method. DSF_Clust deals with three issues that have bedeviled clustering, some dominant sets being statistically determined in a significance level, predefining cluster structure being not required, and the quality of a dominant set being ensured. We have also applied this approach to analyze published data of yeast cell cycle gene expression and found some biologically meaningful gene groups to be dug out. Furthermore, DSF_Clust is a potentially good tool to search for putative regulatory signals.
authors.affiliation: State Key Laboratory of Genetic Engineering, Institute of Genetics, School of Life Science, Fudan University, Shanghai 200433, PR China.
authors.forename: Xuping
authors.initials: X
authors.lastname: Fu
authors.forename: Li
authors.initials: L
authors.lastname: Teng
authors.forename: Yao
authors.initials: Y
authors.lastname: Li
authors.forename: Wenbin
authors.initials: W
authors.lastname: Chen
authors.forename: Yumin
authors.initials: Y
authors.lastname: Mao
authors.forename: I-Fan
authors.initials: IF
authors.lastname: Shen
authors.forename: Yi
authors.initials: Y
authors.lastname: Xie
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D020869:Gene Expression Profiling; D005786:Gene Expression Regulation; D020411:Oligonucleotide Array Sequence Analysis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*