Research Output

Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data

  Rigid adherence to pre-specified thresholds and static graphical representations can lead to incorrect decisions on merging of clusters. As an alternative to existing automated or semi-automated methods, we developed a visual analytics approach for performing hierarchical clustering analysis of short time-series gene expression data. Dynamic sliders control parameters such as the similarity threshold at which clusters are merged and the level of relative intra-cluster distinctiveness, which can be used to identify "weak-edges" within clusters. An expert user can drill down to further explore the dendrogram and detect nested clusters and outliers. This is done by using the sliders and by pointing and clicking on the representation to cut the branches of the tree in multiple-heights. A prototype of this tool has been developed in collaboration with a small group of biologists for analysing their own datasets. Initial feedback on the tool has been positive.

  • Date:

    06 June 2016

  • Publication Status:

    Published

  • DOI:

    10.2312/eurp.20161127

  • Library of Congress:

    QA75 Electronic computers. Computer science

  • Dewey Decimal Classification:

    006.6 Computer graphics

Citation

Vogogias, A., Kennedy, J. & Archambault, D. (2016). Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data. In Isenberg, T. & Sadlo, F. (Eds.). EuroVis 2016 - Posters, 1-3. doi:10.2312/eurp.20161127. ISBN 978-3-03868-015-4

Authors

Keywords

hierarchical; clustering; computer graphics; viewing algorithms; information search and retrieval; information

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