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Physiol. Genomics 13: 31-46, 2003. First published January 7, 2003; doi:10.1152/physiolgenomics.00116.2002
1094-8341/03 $5.00
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Received 4 September 2002; accepted in final form 20 December 2002.
Physiological Genomics 13:31-46 (2003)
1094-8341/03 $5.00 © 2003 American Physiological Society

Expression imbalance map: a new visualization method for detection of mRNA expression imbalance regions

Makoto Kano1, Kunihiro Nishimura2, Shumpei Ishikawa3, Shuichi Tsutsumi3, Koichi Hirota4, Michitaka Hirose4 and Hiroyuki Aburatani3

1 School of Engineering, University of Tokyo, Tokyo 113-8655
2 School of Information Science and Technology, University of Tokyo, Tokyo 113-8655
3 Genome Science Division, Department of Information Systems, Research Center for Advanced Science and Technology, University of Tokyo, 153-8904, Japan
4 Intelligent Cooperative System, Department of Information Systems, Research Center for Advanced Science and Technology, University of Tokyo, 153-8904, Japan

We describe the development of a new visualization method, called the expression imbalance map (EIM), for detecting mRNA expression imbalance regions, reflecting genomic losses and gains at a much higher resolution than conventional technologies such as comparative genomic hybridization (CGH). Simple spatial mapping of the microarray expression profiles on chromosomal location provides little information about genomic structure, because mRNA expression levels do not completely reflect genomic copy number and some microarray probes would be of low quality. The EIM, which does not employ arbitrary selection of thresholds in conjunction with hypergeometric distribution-based algorithm, has a high tolerance of these complex factors. The EIM could detect regionally underexpressed or overexpressed genes (called, here, an expression imbalance region) in lung cancer specimens from their gene expression data of oligonucleotide microarray. Many known as well as potential loci with frequent genomic losses or gains were detected as expression imbalance regions by the EIM. Therefore, the EIM should provide the user with further insight into genomic structure through mRNA expression.

gene expression profiling; allelic imbalance; chromosome mapping; hypergeometric distribution; computing methodologies




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