Email updates

Keep up to date with the latest news and content from BMC Bioinformatics and BioMed Central.

Open Access Highly Accessed Software

HeatMapper: powerful combined visualization of gene expression profile correlations, genotypes, phenotypes and sample characteristics

Roel GW Verhaak1*, Mathijs A Sanders1, Maarten A Bijl1, Ruud Delwel1, Sebastiaan Horsman2, Michael J Moorhouse2, Peter J van der Spek2, Bob Löwenberg and Peter JM Valk1

Author Affiliations

1 Department of Hematology, Erasmus University Medical Center, Rotterdam, The Netherlands

2 Department of Bioinformatics, Erasmus University Medical Center, Rotterdam, The Netherlands

For all author emails, please log on.

BMC Bioinformatics 2006, 7:337  doi:10.1186/1471-2105-7-337

Published: 12 July 2006

Abstract

Background

Accurate interpretation of data obtained by unsupervised analysis of large scale expression profiling studies is currently frequently performed by visually combining sample-gene heatmaps and sample characteristics. This method is not optimal for comparing individual samples or groups of samples. Here, we describe an approach to visually integrate the results of unsupervised and supervised cluster analysis using a correlation plot and additional sample metadata.

Results

We have developed a tool called the HeatMapper that provides such visualizations in a dynamic and flexible manner and is available from http://www.erasmusmc.nl/hematologie/heatmapper/ webcite.

Conclusion

The HeatMapper allows an accessible and comprehensive visualization of the results of gene expression profiling and cluster analysis.