- Oral presentation
- Open Access
Visualization of large microarray experiments with space maps
© Gehlenborg and Brazma; licensee BioMed Central Ltd. 2009
- Published: 19 October 2009
- Gene Expression Profile
- Gene Expression Data
- Context Information
- Microarray Experiment
- Local Pattern
Heatmaps and profile plots are effective techniques to visualize expression profiles of several hundred genes across a few dozen samples. However, these techniques do not scale to data sets with expression profiles that have been measured across several hundred samples or even thousands of samples. Our motivation to find a solution to this scaling problem is based on the observation that with increasingly mature and affordable microarray platforms, the number of studies in ArrayExpress  including hundreds of samples has been increasing steadily over the years.
We have developed the glyph-based Space Maps visualization technique that is conceptually similar to Value and Relation Displays . The technique comprises two steps: (1) Generation of glyphs to represent gene expression profiles and (2) arrangement of the glyphs to reflect relationships between genes. Both steps support the integration of biological knowledge into the visualization, for instance in form of ontologies that describe hierarchical relationships among the conditions in the data. We also use hierarchical organization of samples and aggregation of expression levels to summarize expression values of groups of samples, which enables the user to reduce the amount of data shown on each glyph. Similar to treemaps , this construction makes it possible to start out with an overview of the data and then view details on demand.
The Space Maps visualization technique is a novel approach to visualization of gene expression data that facilitates the visualization of expression profiles of genes with hundreds or thousands of samples without loss of context information. A major strength of this technique is that it allows a tightly coupled exploration of local and global patterns, which makes hypothesis generation more efficient than with traditional techniques.
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