CHI '94 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Readings in information visualization: using vision to think
Readings in information visualization: using vision to think
interactions
Assisted navigation for large information spaces
Proceedings of the conference on Visualization '02
Image-Browser Taxonomy and Guidelines for Designers
IEEE Software
Comparative Multivariate Visualization Across Conceptually Different Graphic Displays
Proceedings of the Seventh International Working Conference on Scientific and Statistical Database Management
Interaction with the Reorderable Matrix
IV '99 Proceedings of the 1999 International Conference on Information Visualisation
When worlds collide: molecular biology as interdisciplinary collaboration
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Proceedings of the Working Conference on Advanced Visual Interfaces
Line graph explorer: scalable display of line graphs using Focus+Context
Proceedings of the working conference on Advanced visual interfaces
A framework for visualization of microarray data and integrated meta information
Information Visualization - Special issue: Bioinformatics visualization
Exploratory visualization of array-based comparative genomic hybridization
Information Visualization - Special issue: Bioinformatics visualization
Visualizing set concordance with permutation matrices and fan diagrams
Interacting with Computers
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We have created VistaClara to explre the effectiveness of applying an extended permutation matrix to the task of exploratory data analysis of multi-experiment microarray studies. The permutation matrix is a visualization technique for interactive exploratory analysis of tabular data that permits both row and column rearrangement, and fits well with the tabular forms of data characteristic of gene expression studies. However, this technique has been largely overlooked by current bioinformatics research. Our implementation supports direct incorporation of supplemental data and annotations into the matrix view. This enables visually searching for patterns in gene expression measurements that correlate with other types of relevant data (disease classes, clinical, histological, drug treatments, etc.). The heatmap visualization common in microarray analysis is extended to provide a novel alternative using size as well as color to graphically represent experimental values, thus allowing more effective quantitative comparisons. Methods to sort rows or columns by similarity extend the possible permutation operations, and allow more efficient searching for biologically relevant patterns in very large data sets. Based on overview+detail principles, a dynamic compressed heatmap view of the entire data set provides the user with overall context, including possible correlations not currently visible in the more detailed view. Combined, these techniques make it possible to perform highly interactive ad hoc visual explorations of microarray.