Biclusters visualization and detection using parallel coordinate plots

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

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-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 languageEnglish
Title of host publicationComputational Models For Life Sciences (CMLS '07) - 2007 International Symposium
Pages114-123
Number of pages10
Volume952
DOIs
Publication statusPublished - 1 Dec 2007
Event2007 International Symposium on Computational Models for Life Sciences, CMLS '07 - Gold Coast, QLD, Australia
Duration: 17 Dec 200719 Dec 2007

Conference

Conference2007 International Symposium on Computational Models for Life Sciences, CMLS '07
Country/TerritoryAustralia
CityGold Coast, QLD
Period17/12/0719/12/07

Keywords

  • Biclustering
  • Bioinformatics
  • Clustering
  • Gene expression data analysis

ASJC Scopus subject areas

  • Physics and Astronomy(all)

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