A new strategy of cooperativity of biclustering and hierarchical clustering: a case of analyzing yeast genomic microarray datasets.
Hierarchical clustering is difficult to be deployed effectively in finding meaningful subtrees since genes rarely exhibit similar expression pattern across a wide range of conditions. It is also difficult to find a suitable level in cleaving a big hierarchy tree. Biclustering is a promising methodology in the field of the analysis of gene expression data of genechip. Generally it can be employed in identification of gene groups, which show a coherent expression profile across a subset of conditions. But in some cases of biclustering analysis of gene expressions, the genes in one bicluster are involved in more than one functional group, or all genes in one bicluster are involved in unknown functional groups (e.g. pattern VI and VIII in our studies). Then, how to predict the function of genes in these patterns? In the present research, we developed a new strategy of combining both of the clustering methods, hierarchical clustering and biclustering. The reserved conditions in datasets for hierarchical clustering were elicited according to the conditions in biclusters, and after hierarchical clustering, more detailed results in predicting unknown genes in certain patterns were obtained. This strategy of cooperating both of the methods during clustering procedure should be an effective guideline for functional predictions.
Mao
Daqing
D
Department of Biochemistry, School of Pharmaceutical Engineering, Shenyang Pharmaceutical University, Shenyang 110016, China.
Luo
Yi
Y
Zhang
Jinghai
J
Zhu
Jun
J
D016000:Cluster Analysis; D030562:Databases, Protein; D005656:Fungal Proteins / Q000235:genetics / Q000378:metabolism*; D020869:Gene Expression Profiling; D016681:Genome, Fungal; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism*
D005656:Fungal Proteins