Biclusters visualization and detection using parallel coordinate plots
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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.
Proceedings of the International Symposium on Computational Models for Life Sciences
Copyright 2007 American Institute of Physics. The attached file is reproduced here in accordance with the copyright policy of the publisher. Use hypertext link for access to the conference website.