High Dimensional Brushing for Interactive Exploration of Multivariate Data
VIS '95 Proceedings of the 6th conference on Visualization '95
An Insight-Based Methodology for Evaluating Bioinformatics Visualizations
IEEE Transactions on Visualization and Computer Graphics
Java Treeview---extensible visualization of microarray data
Bioinformatics
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Visual Analytics: Definition, Process, and Challenges
Information Visualization
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Comparative Analysis of Multidimensional, Quantitative Data
IEEE Transactions on Visualization and Computer Graphics
Brushing Dimensions—A Dual Visual Analysis Model for High-Dimensional Data
IEEE Transactions on Visualization and Computer Graphics
iPCA: an interactive system for PCA-based visual analytics
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
Selecting good views of high-dimensional data using class consistency
EuroVis'09 Proceedings of the 11th Eurographics / IEEE - VGTC conference on Visualization
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Microarray data represents the expression levels of genes for different samples and for different conditions. It has been a central topic in bioinformatics research for a long time already. Researchers try to discover groups of genes that are responsible for specific biological processes. Statistical analysis tools and visualizations have been widely used in the analysis of microarray data. Researchers try to build hypotheses on both the genes and the samples. Therefore, such analyses require the joint exploration of the genes and the samples. However, current methods in interactive visual analysis fail to provide the necessary mechanisms for this joint analysis. In this paper, we propose an interactive visual analysis framework that enables the dual analysis of the samples and the genes through the use of integrated statistical tools. We introduce a set of specialized views and a detailed analysis procedure to describe the utilization of our framework.