Clustering analysis for gene expression data

Yidong Chen; Olga Ermolaeva; Michael L. Bittner; Paul S. Meltzer; Jeffrey M. Trent; Edward R. Dougherty; Sinan Batman

doi:10.1117/12.347541

3 May 1999 Clustering analysis for gene expression data

Yidong Chen, Olga Ermolaeva, Michael L. Bittner, Paul S. Meltzer, Jeffrey M. Trent, Edward R. Dougherty, Sinan Batman

Proceedings Volume 3602, Advances in Fluorescence Sensing Technology IV; (1999) https://doi.org/10.1117/12.347541
Event: BiOS '99 International Biomedical Optics Symposium, 1999, San Jose, CA, United States

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.

Citation Download Citation

Yidong Chen, Olga Ermolaeva, Michael L. Bittner, Paul S. Meltzer, Jeffrey M. Trent, Edward R. Dougherty, and Sinan Batman "Clustering analysis for gene expression data", Proc. SPIE 3602, Advances in Fluorescence Sensing Technology IV, (3 May 1999); https://doi.org/10.1117/12.347541

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