A method for clustering gene expression data based on graph structure.
Recently, gene expression data under various conditions have largely been obtained by the utilization of the DNA microarrays and oligonucleotide arrays. There have been emerging demands to analyze the function of genes from the gene expression profiles. For clustering genes from their expression profiles, hierarchical clustering has been widely used. The clustering method represents the relationships of genes as a tree structure by connecting genes using their similarity scores based on the Pearson correlation coefficient. But the clustering method is sensitive to experimental noise. To cope with the problem, we propose another type of clustering method (the p-quasi complete linkage clustering). We apply this method to the gene expression data of yeast cell-cycles and human lung cancer. The effectiveness of our method is demonstrated by comparing clustering results with other methods.
Seno
Shigeto
S
Department of Bioinformatic Engineering, Graduate School of Information Science and Technology, Osaka University, 1-3 Machikaneyama, Toyonaka, Osaka 560-8531, Japan. s-senoo@ist.osaka-u.ac.jp
Teramoto
Reiji
R
Takenaka
Yoichi
Y
Matsuda
Hideo
H
D000465:Algorithms; D001185:Artificial Intelligence; D018797:Cell Cycle Proteins / Q000235:genetics*; D016000:Cluster Analysis; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000235:genetics* / Q000378:metabolism; D009363:Neoplasm Proteins / Q000502:physiology*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D029701:Saccharomyces cerevisiae Proteins / Q000235:genetics*
D018797:Cell Cycle Proteins; D009363:Neoplasm Proteins; D029701:Saccharomyces cerevisiae Proteins