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.
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2 November 2007
COMPUTATIONAL MODELS FOR LIFE SCIENCES—CMLS '07: 2007 International Symposium on Computational Models of Life Sciences
17–19 December 2007
Gold Coast, Queensland (Australia)
Research Article|
November 02 2007
Biclusters Visualization and Detection Using Parallel Coordinate Plots
K. O. Cheng;
K. O. Cheng
aCentre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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N. F. Law;
N. F. Law
aCentre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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W. C. Siu;
W. C. Siu
aCentre for Signal Processing, Department of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
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A. W. C. Liew
A. W. C. Liew
bSchool of Information and Communication Technology, Griffith University, Gold Coast Campus, QLD 4222, Queensland Australia
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AIP Conf. Proc. 952, 114–123 (2007)
Citation
K. O. Cheng, N. F. Law, W. C. Siu, A. W. C. Liew; Biclusters Visualization and Detection Using Parallel Coordinate Plots. AIP Conf. Proc. 2 November 2007; 952 (1): 114–123. https://doi.org/10.1063/1.2816614
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