Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

Gad Getz; Hilah Gal; Itai Kela; Daniel A. Notterman; Eytan Domany

doi:10.1093/bioinformatics/btf876

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

Gad Getz, Hilah Gal, Itai Kela, Daniel A. Notterman, Eytan Domany

Molecular Biology

Research output: Contribution to journal › Review article › peer-review

59 Scopus citations

Abstract

We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

Original language	English (US)
Pages (from-to)	1079-1089
Number of pages	11
Journal	Bioinformatics
Volume	19
Issue number	9
DOIs	https://doi.org/10.1093/bioinformatics/btf876
State	Published - Jun 12 2003

All Science Journal Classification (ASJC) codes

Computational Mathematics
Molecular Biology
Biochemistry
Statistics and Probability
Computer Science Applications
Computational Theory and Mathematics

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10.1093/bioinformatics/btf876

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title = "Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data",

abstract = "We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.",

author = "Gad Getz and Hilah Gal and Itai Kela and Notterman, {Daniel A.} and Eytan Domany",

note = "Funding Information: This research was partially supported by grants from the Germany—Israel Science Foundation (GIF), the Israel Academy of Sciences (ISF), the NIH under grant no. #5 P01 CA 65930-06 and the Ridgefield Foundation. We thank D. Botstein for directing us to the two papers of the Stanford group on breast cancer (PAL and SAL).",

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doi = "10.1093/bioinformatics/btf876",

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T1 - Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

AU - Getz, Gad

AU - Gal, Hilah

AU - Kela, Itai

AU - Notterman, Daniel A.

AU - Domany, Eytan

N1 - Funding Information: This research was partially supported by grants from the Germany—Israel Science Foundation (GIF), the Israel Academy of Sciences (ISF), the NIH under grant no. #5 P01 CA 65930-06 and the Ridgefield Foundation. We thank D. Botstein for directing us to the two papers of the Stanford group on breast cancer (PAL and SAL).

PY - 2003/6/12

Y1 - 2003/6/12

N2 - We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

AB - We present and review coupled two-way clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

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U2 - 10.1093/bioinformatics/btf876

DO - 10.1093/bioinformatics/btf876

M3 - Review article

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JO - Bioinformatics

JF - Bioinformatics

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ER -

Coupled two-way clustering analysis of breast cancer and colon cancer gene expression data

Abstract

All Science Journal Classification (ASJC) codes

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