Hierarchical Clustering Explorer

screen shot of hce

Because of the complex multidimensional nature of gene expression data, hierarchical clustering is often applied to find meaningful patterns, clusters and outliers. Identifying clusters of genes that are activated with malignant as opposed to benign melanoma (skin cancer) may contribute to understand disease processes that could lead to successful interventions to treat or prevent these diseases. With 5,000 to 40,000 genes and 5-100 samples in a given study, finding the influential cluster can be a daunting task.

The standard way of finding related groups of genes is through hierarchical clustering. Our Hierarchical Clustering Explorer provides a high degree of interactivity, compared to other software tools, thereby enabling researchers to test hypothesis and explore the vast data spaces rapidly. Our work is conducted in conjunction with the Research Center for Genetic Medicine at the Children's National Medical Center, in cooperation with Dr. Eric Hoffman. He has provided support under grant N01 NS-1-2339 from the National Institutes for Health. HCE 2.0 is available to researchers and students to conduct their own research.

For more information, see the HCE homepage.

Website designed by Harry Hochheiser