Abstract
We propose a visual analysis approach that employs a matrix-based visualization technique to explore relations between annotation terms in biological data sets. Our flexible framework provides various ways to form combinations of data elements, which results in a co-occurrence matrix. Each cell in this matrix stores a list of items associated with the combination of the corresponding row and column element. By re-arranging the rows and columns of this matrix, and color-coding the cell contents, patterns become visible. Our prototype tool COMBat allows users to construct a new matrix on the fly by selecting subsets of items of interest, or filtering out uninteresting ones, and it provides variousadditional interaction techniques. We illustrate our approach with a few case studies concerning the identification of functional links between the presence of particular genes or genomic sequences and particular cellular processes.
Original language | English |
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Title of host publication | 2013 IEEE Symposium on Biological Data Visualization (BioVis, Atlanta GA, USA, October 13-14, 2013) |
Editors | J. Roerdink, J. Kennedy |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 17-24 |
ISBN (Print) | 978-1-4799-1659-7 |
DOIs | |
Publication status | Published - 2013 |
Event | 3rd IEEE Symposium on Biological Data Visualization (BioVis 2013), October 13-14, 2013, Atlanta, GA, USA - Atlanta, GA, United States Duration: 13 Oct 2013 → 14 Oct 2013 http://biovis.net/year/2013/about.html |
Conference
Conference | 3rd IEEE Symposium on Biological Data Visualization (BioVis 2013), October 13-14, 2013, Atlanta, GA, USA |
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Abbreviated title | BioVis 2013 |
Country/Territory | United States |
City | Atlanta, GA |
Period | 13/10/13 → 14/10/13 |
Internet address |