Biclusters visualization and detection using parallel coordinate plots

K. O. Cheng; Ngai Fong Law; W. C. Siu; A. W.C. Liew

doi:10.1063/1.2816614

Biclusters visualization and detection using parallel coordinate plots

K. O. Cheng, Ngai Fong Law, W. C. Siu, A. W.C. Liew

The Hong Kong Polytechnic University

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

4 Citations (Scopus)

Abstract

The parallel coordinate (PC) plot is a powerful visualization tools for high-dimensional data. In this paper, we explore its usage on gene expression data analysis. We found that both the additive-related and the multiplicative-related coherent genes exhibit special patterns in the PC plots. One-dimensional clustering can then be applied to detect these patterns. Besides, a split-and-merge mechanism is employed to find the biggest coherent subsets inside the gene expression matrix. Experimental results showed that our proposed algorithm is effective in detecting various types of biclusters. In addition, the biclustering results can be visualized under a 2D setting, in which objective and subjective cluster quality evaluation can be performed.

Original language	English
Title of host publication	Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages	114-123
Number of pages	10
Volume	952
DOIs	https://doi.org/10.1063/1.2816614
Publication status	Published - 1 Dec 2007
Event	2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia Duration: 17 Dec 2007 → 19 Dec 2007

Conference

Conference	2007 International Symposium on Computational Models for Life Sciences, CMLS '07
Country/Territory	Australia
City	Gold Coast, QLD
Period	17/12/07 → 19/12/07

Keywords

Biclustering
Bioinformatics
Clustering
Gene expression data analysis

ASJC Scopus subject areas

General Physics and Astronomy

More information

10.1063/1.2816614

Cite this

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title = "Biclusters visualization and detection using parallel coordinate plots",

abstract = "The parallel coordinate (PC) plot is a powerful visualization tools for high-dimensional data. In this paper, we explore its usage on gene expression data analysis. We found that both the additive-related and the multiplicative-related coherent genes exhibit special patterns in the PC plots. One-dimensional clustering can then be applied to detect these patterns. Besides, a split-and-merge mechanism is employed to find the biggest coherent subsets inside the gene expression matrix. Experimental results showed that our proposed algorithm is effective in detecting various types of biclusters. In addition, the biclustering results can be visualized under a 2D setting, in which objective and subjective cluster quality evaluation can be performed.",

keywords = "Biclustering, Bioinformatics, Clustering, Gene expression data analysis",

author = "Cheng, {K. O.} and Law, {Ngai Fong} and Siu, {W. C.} and Liew, {A. W.C.}",

year = "2007",

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doi = "10.1063/1.2816614",

language = "English",

isbn = "9780735404663",

volume = "952",

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Cheng, KO, Law, NF, Siu, WC & Liew, AWC 2007, Biclusters visualization and detection using parallel coordinate plots. in Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium. vol. 952, pp. 114-123, 2007 International Symposium on Computational Models for Life Sciences, CMLS '07, Gold Coast, QLD, Australia, 17/12/07. https://doi.org/10.1063/1.2816614

Biclusters visualization and detection using parallel coordinate plots. / Cheng, K. O.; Law, Ngai Fong; Siu, W. C. et al.
Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium. Vol. 952 2007. p. 114-123.

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

TY - GEN

T1 - Biclusters visualization and detection using parallel coordinate plots

AU - Cheng, K. O.

AU - Law, Ngai Fong

AU - Siu, W. C.

AU - Liew, A. W.C.

PY - 2007/12/1

Y1 - 2007/12/1

N2 - The parallel coordinate (PC) plot is a powerful visualization tools for high-dimensional data. In this paper, we explore its usage on gene expression data analysis. We found that both the additive-related and the multiplicative-related coherent genes exhibit special patterns in the PC plots. One-dimensional clustering can then be applied to detect these patterns. Besides, a split-and-merge mechanism is employed to find the biggest coherent subsets inside the gene expression matrix. Experimental results showed that our proposed algorithm is effective in detecting various types of biclusters. In addition, the biclustering results can be visualized under a 2D setting, in which objective and subjective cluster quality evaluation can be performed.

AB - The parallel coordinate (PC) plot is a powerful visualization tools for high-dimensional data. In this paper, we explore its usage on gene expression data analysis. We found that both the additive-related and the multiplicative-related coherent genes exhibit special patterns in the PC plots. One-dimensional clustering can then be applied to detect these patterns. Besides, a split-and-merge mechanism is employed to find the biggest coherent subsets inside the gene expression matrix. Experimental results showed that our proposed algorithm is effective in detecting various types of biclusters. In addition, the biclustering results can be visualized under a 2D setting, in which objective and subjective cluster quality evaluation can be performed.

KW - Biclustering

KW - Bioinformatics

KW - Clustering

KW - Gene expression data analysis

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U2 - 10.1063/1.2816614

DO - 10.1063/1.2816614

M3 - Conference article published in proceeding or book

SN - 9780735404663

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SP - 114

EP - 123

BT - Computational Models For Life Sciences (CMLS '07) - 2007 International Symposium

T2 - 2007 International Symposium on Computational Models for Life Sciences, CMLS '07

Y2 - 17 December 2007 through 19 December 2007

ER -

Biclusters visualization and detection using parallel coordinate plots

Abstract

Conference

Keywords

ASJC Scopus subject areas

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