PointCloudXplore: visual analysis of 3d gene expression data using physical views and parallel coordinates

Authors:
O. Rübel;G. H. Weber;S. V. E. Keränen;C. C. Fowlkes;C. L. Luengo Hendriks;L. Simirenko;N. Y. Shah;M. B. Eisen;M. D. Biggin;H. Hagen;D. Sudar;J. Malik;D. W. Knowles;B. Hamann
Affiliations:
International Research Training Group "Visualization of Large and Unstructured Data Sets," University of Kaiserslautern, Germany and Institute for Data Analysis and Visualization, University of Ca ...;Institute for Data Analysis and Visualization, University of California, Davis and Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Computer Science Division, University of California, Berkeley, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Institute for Data Analysis and Visualization, University of California, Davis, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;International Research Training Group "Visualization of Large and Unstructured Data Sets," University of Kaiserslautern, Germany;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;Computer Science Division, University of California, Berkeley, CA;Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, CA;International Research Training Group "Visualization of Large and Unstructured Data Sets," University of Kaiserslautern, Germany and Institute for Data Analysis and Visualization, University of Ca ...
Venue:
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Year:
2006

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Abstract

To allow a more rigorous understanding of animal gene regulatory networks, the Berkeley Drosophila Transcription Network Project (BDTNP) has developed a suite of methods that support quantitative, computational analysis of three-dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos. Here we report the first components of a visualization tool, PointCloudXplore, that allows the relationships between different gene's expression to be analyzed using the BDTNP's datasets. PointCloudXplore uses the established visualization techniques of multiple views, brushing, and linking to support the analysis of high-dimensional datasets that describe many genes' expression. Each of the views in PointCloud- Xplore shows a different gene expression data property. Brushing is used to select and emphasize data associated with defined subsets of embryo cells within a view. Linking is used to show in additional views the expression data for a group of cells that have first been highlighted as a brush in a single view, allowing further data subset properties to be determined. In PointCloudXplore, physical views of the data are linked to parallel coordinates. Physical views show the spatial relationships between different genes' expression patterns within the embryo. Parallel coordinates, on the other hand, show only some features of each gene's expression, but allow simultaneous analysis of data for many more genes than would be possible in a physical view. We have developed several extensions to standard parallel coordinates to facilitate brushing the visualization of 3D gene expression data.