Tree-based analysis of microarray data for classifying breast cancer.
DNA microarray data have provided us with the opportunity to assess the expression levels for thousands of genes simultaneously. One of the uses of this information is to classify cancer tumors. A noted challenge in using microarray information is analytical. Following the work of Zhang et al. (1), we further pursue the use of recursive partitioning in analyses of microarray data for cancer classification. Not only does the recursive partitioning technique create intuitive classification rules, but also it is most flexible as to the handling of a massive number of genes, missing expressions, and multi-class tissues. Using a published data set (2), we demonstrate that the recursive partitioning technique creates a more precise and simpler classification rule than other commonly used approaches. In particular, we introduce the concept of A-tree and propose a procedure to assess a large number of A-trees. One of the identified genes (ERBB2) is in the close region of BRCA1 (17q21.1) and has been shown by others to have altered expression levels in breast cancer. Nonetheless, our identified genes warrant further investigation as to whether they play a role in the etiology of breast cancer.
Zhang
Heping
H
Department of Epidemiology and Public Health, Yale University School of Medicine, New Haven, CT 06520-8034, USA. heping.zhang@yale.edu
Yu
Chang-Yung
CY
D001943:Breast Neoplasms / Q000145:classification / Q000235:genetics*; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D008954:Models, Biological; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*