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Proceedings Paper

Clustering analysis for gene expression data
Author(s): Yidong Chen; Olga Ermolaeva; Michael L. Bittner; Paul S. Meltzer; Jeffrey M. Trent; Edward R. Dougherty; Sinan Batman
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Paper Abstract

The recent development of cDNA microarray allows ready access to large amount gene expression patterns for many genetic materials. Gene expression of tissue samples can be quantitatively analyzed by hybridizing fluor-tagged mRNA to targets on a cDNA microarray. Ratios of average expression level arising from co-hybridized normal and pathological samples are extracted via image segmentation, thus the gene expression pattern are obtained. The gene expression in a given biological process may provide a fingerprint of the sample development, or response to certain treatment. We propose a K-mean based algorithm in which gene expression levels fluctuate in parallel will be clustered together. The resulting cluster suggests some functional relationships between genes, and some known genes belongs to a unique functional classes shall provide indication for unknown genes in the same clusters.

Paper Details

Date Published: 3 May 1999
PDF: 7 pages
Proc. SPIE 3602, Advances in Fluorescence Sensing Technology IV, (3 May 1999); doi: 10.1117/12.347541
Show Author Affiliations
Yidong Chen, National Institutes of Health (United States)
Olga Ermolaeva, National Institutes of Health (United States)
Michael L. Bittner, National Institutes of Health (United States)
Paul S. Meltzer, National Institutes of Health (United States)
Jeffrey M. Trent, National Institutes of Health (United States)
Edward R. Dougherty, Texas A&M Univ. (United States)
Sinan Batman, Texas A&M Univ. (United States)


Published in SPIE Proceedings Vol. 3602:
Advances in Fluorescence Sensing Technology IV
Joseph R. Lakowicz; Steven A. Soper; Richard B. Thompson, Editor(s)

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