Cancer DNA microarray analysis considering multi-subclass with graph-based clustering method.
It is well known that various genes related to cell cycle, cell-cell adhesion, and transcriptional regulation cause the onset of cancer. Moreover, environmental factors including age, sex, and lifestyle can also contribute to the onset of cancer. Therefore, it is difficult to ascertain which factors influence the onset. Thus, patients suffering from same disease can be divided into several distinct groups. In the present study, we applied graph-based clustering to several DNA microarray datasets before the classification analysis. Several clusters formed by the graph-based clustering were used for the construction of multi-class classification model with the k-nearest neighbor and for finding genes, which are specific to a certain cluster, by One vs. Others classification. Using this approach, the classification model was constructed for four microarray datasets, leukemia, breast cancer, prostate cancer, and colon cancer, and the accuracies of classification with k-nearest neighbor were all more than 80%. And in the breast cancer dataset, we succeeded in finding genes that are specific in a cluster consisting of 38 control group samples. These results indicate the importance of sample clustering before classification model construction.
Kawamura
Takashi
T
Department of Biotechnology, School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
Mutoh
Hironori
H
Tomita
Yasuyuki
Y
Kato
Ryuji
R
Honda
Hiroyuki
H
D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008297:Male; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results