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HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample
Roel G W Verhaak1, Mathijs A Sanders, Maarten A Bijl
1Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands. r.verhaak@erasmusmc.nl
BMC Bioinformatics
|July 14, 2006
Summary
This study introduces HeatMapper, a novel tool that enhances gene expression profiling by integrating unsupervised and supervised cluster analysis. HeatMapper offers improved visualization for comparing samples and interpreting large-scale expression data.
Area of Science:
- Bioinformatics
- Computational Biology
- Genomics
Background:
- Interpreting large-scale gene expression data often relies on visual analysis of heatmaps, which can be suboptimal for sample comparisons.
- Current methods struggle with effectively integrating unsupervised and supervised clustering results with sample metadata.
Purpose of the Study:
- To develop a more effective visualization approach for gene expression profiling data.
- To improve the comparison of individual samples and groups within large-scale studies.
Main Methods:
- Developed a novel visualization tool, HeatMapper.
- Integrated unsupervised and supervised cluster analysis results.
- Utilized correlation plots and additional sample metadata for visualization.
Main Results:
- The HeatMapper tool provides dynamic and flexible visualizations.
- It facilitates a comprehensive overview of gene expression profiling and cluster analysis outcomes.
- The tool is accessible at http://www.erasmusmc.nl/hematologie/heatmapper/.
Conclusions:
- HeatMapper offers an accessible and comprehensive method for visualizing gene expression profiling results.
- The tool enhances the interpretation of complex biological datasets.
