HOME HELP FEEDBACK SUBSCRIPTIONS ARCHIVE SEARCH TABLE OF CONTENTS		QUICK SEARCH:   [advanced] Author: Keyword(s): Year:  Vol:  Page:

This Article Full Text Full Text (PDF) All Versions of this Article: 13/1/31    most recent 00116.2002v1 Alert me when this article is cited Alert me if a correction is posted Citation Map Services Email this article to a friend Similar articles in this journal Similar articles in Web of Science Similar articles in PubMed Alert me to new issues of the journal Download to citation manager Citing Articles Citing Articles via HighWire Citing Articles via Web of Science (20) Citing Articles via Google Scholar Google Scholar Articles by Kano, M. Articles by Aburatani, H. Search for Related Content PubMed PubMed Citation Articles by Kano, M. Articles by Aburatani, H.

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

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

This article has been cited by other articles:

				K. De Preter, R. Barriot, F. Speleman, J. Vandesompele, and Y. Moreau Positional gene enrichment analysis of gene sets for high-resolution identification of overrepresented chromosomal regions Nucleic Acids Res., April 1, 2008; 36(7): e43 - e43. [Abstract] [Full Text] [PDF]

				A. Callegaro, D. Basso, and S. Bicciato A locally adaptive statistical procedure (LAP) to identify differentially expressed chromosomal regions Bioinformatics, November 1, 2006; 22(21): 2658 - 2666. [Abstract] [Full Text] [PDF]

				A. M. Levin, D. Ghosh, K. R. Cho, and S. L. R. Kardia A model-based scan statistic for identifying extreme chromosomal regions of gene expression in human tumors Bioinformatics, June 15, 2005; 21(12): 2867 - 2874. [Abstract] [Full Text] [PDF]

				Y. Midorikawa, S. Tsutsumi, K. Nishimura, N. Kamimura, M. Kano, H. Sakamoto, M. Makuuchi, and H. Aburatani Distinct Chromosomal Bias of Gene Expression Signatures in the Progression of Hepatocellular Carcinoma Cancer Res., October 15, 2004; 64(20): 7263 - 7270. [Abstract] [Full Text] [PDF]