Clustering analysis for gene expression data

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

Clustering analysis for gene expression data

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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 cohybridized 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.

Original language	English (US)
Title of host publication	Proceedings of SPIE - The International Society for Optical Engineering
Publisher	Society of Photo-Optical Instrumentation Engineers
Pages	422-428
Number of pages	7
ISBN (Print)	0819430722
State	Published - 1999
Externally published	Yes
Event	Proceedings of the 1999 Advances in Fluorescence Sensing Technology - San Jose, CA, USA Duration: Jan 24 1999 → Jan 27 1999

Publication series

Name	Proceedings of SPIE - The International Society for Optical Engineering
Volume	3602
ISSN (Print)	0277-786X

Other

Other	Proceedings of the 1999 Advances in Fluorescence Sensing Technology
City	San Jose, CA, USA
Period	1/24/99 → 1/27/99

ASJC Scopus subject areas

Electronic, Optical and Magnetic Materials
Condensed Matter Physics
Computer Science Applications
Applied Mathematics
Electrical and Electronic Engineering

Cite this

Chen, Y., Ermolaeva, O., Bittner, M., Meltzer, P., Trent, J., Dougherty, E. R., & Batman, S. (1999). Clustering analysis for gene expression data. In Proceedings of SPIE - The International Society for Optical Engineering (pp. 422-428). (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 3602). Society of Photo-Optical Instrumentation Engineers.

Clustering analysis for gene expression data. / Chen, Yidong; Ermolaeva, Olga; Bittner, Michael et al.
Proceedings of SPIE - The International Society for Optical Engineering. Society of Photo-Optical Instrumentation Engineers, 1999. p. 422-428 (Proceedings of SPIE - The International Society for Optical Engineering; Vol. 3602).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Chen, Y, Ermolaeva, O, Bittner, M, Meltzer, P, Trent, J, Dougherty, ER & Batman, S 1999, Clustering analysis for gene expression data. in Proceedings of SPIE - The International Society for Optical Engineering. Proceedings of SPIE - The International Society for Optical Engineering, vol. 3602, Society of Photo-Optical Instrumentation Engineers, pp. 422-428, Proceedings of the 1999 Advances in Fluorescence Sensing Technology, San Jose, CA, USA, 1/24/99.

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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 cohybridized 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.",

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publisher = "Society of Photo-Optical Instrumentation Engineers",

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AU - Chen, Yidong

AU - Ermolaeva, Olga

AU - Bittner, Michael

AU - Meltzer, Paul

AU - Trent, Jeffrey

AU - Dougherty, Edward R.

AU - Batman, Sinan

PY - 1999

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AB - 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 cohybridized 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.

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T3 - Proceedings of SPIE - The International Society for Optical Engineering

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BT - Proceedings of SPIE - The International Society for Optical Engineering

PB - Society of Photo-Optical Instrumentation Engineers

T2 - Proceedings of the 1999 Advances in Fluorescence Sensing Technology

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ER -

Clustering analysis for gene expression data

Abstract

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ASJC Scopus subject areas

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