PointCloudXplore: a visualization tool for 3D gene expressiondata

PointCloudXplore: A Visualization Tool for 3D Gene Expression Data Oliver R¨ bel ∗,1,2 , Gunther H. Weber 2,3 , Soile V.E. Ker¨ nen 3 , Charless C. Fowlkes 4 , u a Cris L. Luengo Hendriks 3 , Lisa Simirenko 3 , Nameeta Y. Shah 2 , Michael B. Eisen 3 , Mark D. Biggin 3 , Hans Hagen 1 , Damir Sudar 3 , Jitendra Malik 4 , David W. Knowles 3 , and Bernd Hamann 1,2 International Research Training Group “Visualization of Large and Unstructured Data Sets,” University of Kaiserslautern, Germany Institute for Data Analysis and Visualization, University of California, Davis, CA, USA Life Sciences and Genomics Divisions, Lawrence Berkeley National Laboratory, Berkeley, CA, USA Computer Science Division, University of California, Berkeley, CA, USA Abstract: The Berkeley Drosophila Transcription Network Project (BDTNP) has de- veloped a suite of methods that support quantitative, computational analysis of three- dimensional (3D) gene expression patterns with cellular resolution in early Drosophila embryos, aiming at a more in-depth understanding of gene regulatory networks. We describe a new tool, called PointCloudXplore (PCX), that supports effective 3D gene expression data exploration. PCX is a visualization tool that 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 PointCloudXplore shows a different gene expression data property. Brushing is used to select and em- phasize 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 PCX, physical views of the data are linked to abstract data displays such as parallel coordinates. Physical views show the spatial relationships between different genes’ expression patterns within an embryo. Abstract gene expression data displays on the other hand allow for an analysis of relationships between different genes directly in the gene expression space. We discuss on parallel coordinates as one example abstract data view currently available in PCX. We have developed several ex- tensions to standard parallel coordinates to facilitate brushing and the visualization of 3D gene expression data. ∗ oliverruebel@web.de

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