Computer graphics: principles and practice (2nd ed.)
Computer graphics: principles and practice (2nd ed.)
Choosing effective colours for data visualization
Proceedings of the 7th conference on Visualization '96
Feature detection in linked derived spaces
Proceedings of the conference on Visualization '98
Guidelines for using multiple views in information visualization
AVI '00 Proceedings of the working conference on Advanced visual interfaces
Proceedings of the conference on Visualization '00
Interactive feature specification for focus+context visualization of complex simulation data
VISSYM '03 Proceedings of the symposium on Data visualisation 2003
Interactive Focus+Context Visualization with Linked 2D/3D Scatterplots
CMV '04 Proceedings of the Second International Conference on Coordinated & Multiple Views in Exploratory Visualization
CSBW '05 Proceedings of the 2005 IEEE Computational Systems Bioinformatics Conference - Workshops
EUROVIS'06 Proceedings of the Eighth Joint Eurographics / IEEE VGTC conference on Visualization
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
A visual analytics framework for cluster analysis of DNA microarray data
Expert Systems with Applications: An International Journal
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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.