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Visual Exploration of Three-Dimensional Gene Expression Using Physical Views and Linked Abstract Views
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Source IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) archive
Volume 6 ,  Issue 2  (April 2009) table of contents
Pages 296-309  
Year of Publication: 2009
ISSN:1545-5963
Authors
Gunther H. Weber  Lawrence Berkeley National Laboratory, Berkeley
Oliver Rubel  (IRTG), University of Kaiserslautern, Kaiserslautern
Min-Yu Huang  University of California, Davis, Davis
Angela H. DePace  Harvard Medical School, Boston
Charless C. Fowlkes  University of California, Irvine, Irvine
Soile V. E. Keranen  Lawrence Berkeley National Laboratory, Berkeley
Cris L. Luengo Hendriks  Uppsala University, Uppsala
Hans Hagen  University of Kaiserslautern, Kaiserslautern
David W. Knowles  Lawrence Berkeley National Laboratory, Berkeley
Jitendra Malik  University of California, Berkeley, Berkeley
Mark D. Biggin  Lawrence Berkeley National Laboratory, Berkeley
Bernd Hamann  University of California, Davis, Davis
Publisher
IEEE Computer Society Press  Los Alamitos, CA, USA
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DOI Bookmark: 10.1109/TCBB.2007.70249

ABSTRACT

During animal development, complex patterns of gene expression provide positional information within the embryo. To better understand the underlying gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed methods that support quantitative computational analysis of three-dimensional (3D) gene expression in early Drosophila embryos at cellular resolution. We introduce PointCloudXplore (PCX), an interactive visualization tool that supports visual exploration of relationships between different genes' expression using a combination of established visualization techniques. Two aspects of gene expression are of particular interest: 1) gene expression patterns defined by the spatial locations of cells expressing a gene and 2) relationships between the expression levels of multiple genes. PCX provides users with two corresponding classes of data views: 1) Physical Views based on the spatial relationships of cells in the embryo and 2) Abstract Views that discard spatial information and plot expression levels of multiple genes with respect to each other. Cell Selectors highlight data associated with subsets of embryo cells within a View. Using linking, these selected cells can be viewed in multiple representations. We describe PCX as a 3D gene expression visualization tool and provide examples of how it has been used by BDTNP biologists to generate new hypotheses.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Collaborative Colleagues:
Gunther H. Weber: colleagues
Oliver Rubel: colleagues
Min-Yu Huang: colleagues
Angela H. DePace: colleagues
Charless C. Fowlkes: colleagues
Soile V. E. Keranen: colleagues
Cris L. Luengo Hendriks: colleagues
Hans Hagen: colleagues
David W. Knowles: colleagues
Jitendra Malik: colleagues
Mark D. Biggin: colleagues
Bernd Hamann: colleagues