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

Vogogias, Athanasios; Kennedy, Jessie; Archambault, D

Abstract

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.

Citation

Vogogias, A., Kennedy, J., & Archambault, D. (2016, July). Hierarchical Clustering with Multiple-Height Branch-Cut Applied to Short Time-Series Gene Expression Data. Presented at Eurovis 2016

• Presentation Conference Type: Conference Paper (published)
• Conference Name: Eurovis 2016
• Start Date: Jul 6, 2016
• End Date: Jul 10, 2016
• Acceptance Date: Apr 22, 2016
• Publication Date: Jun 6, 2016
• Deposit Date: Jul 18, 2016
• Publicly Available Date: Sep 20, 2016
• Journal: Eurographics Conference on Visualization (EuroVis)
• Pages: 1-3
• Book Title: EuroVis 2016 - Posters
• ISBN: 978-3-03868-015-4
• DOI: https://doi.org/10.2312/eurp.20161127
• Keywords: hierarchical; clustering; computer graphics; viewing algorithms; information search and retrieval; information
• Public URL: http://researchrepository.napier.ac.uk/Output/301145
• Related Public URLs: http://diglib.eg.org/handle/10.2312/15276
• Contract Date: Sep 20, 2016