Heat map visualizations allow comparison of multiple clustering results and evaluation of dataset quality: Application to microarray data

John Sharko*, Georges G. Grinstein, Kenneth A. Marx, Jianping Zhou, Chia Ho Cheng, Shannon Odelberg, Hans-Georg Simon

*Corresponding author for this work

Research output: Contribution to journalConference article

11 Scopus citations

Abstract

Since clustering algorithms are heuristic, multiple clustering algorithms applied to the same dataset will typically not generate the same sets of clusters. This is especially true for complex datasets such as those from microarray time series experiments. Two such microarray datasets describing gene expression activities from regenerating newt forelimbs at various times following limb amputation were used in this study. A cluster stability matrix, which shows the number of times two genes appear in the same cluster, was generated as a heat map. This was used to evaluate the overall variation among the clustering algorithms and to identify similar clusters. A comparison of the cluster stability matrices for two related microarray experiments with different levels of precision was shown to be an effective basis for comparing the quality of the two sets of experiments. A pairwise heat map was generated to show which pairs of clustering algorithms grouped the data into similar clusters.

Original languageEnglish (US)
Article number4272030
Pages (from-to)521-526
Number of pages6
JournalProceedings of the International Conference on Information Visualisation
DOIs
StatePublished - Oct 23 2007
Event11th International Conference Information Visualization, IV 2007 - Zurich, Switzerland
Duration: Jul 4 2007Jul 6 2007

ASJC Scopus subject areas

  • Computer Science(all)
  • Engineering(all)

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