HCIL Logo  Human-Computer Interaction Lab / University of Maryland
 about hcil 

Hierarchical Clustering Explorer 2.0

About This Project | HCE 3.0 (HCE2W) | HCE 2.0 | HCE 1.0 | Download | User Manual


Multidimensional data sets are common in many research areas, including microarray experiment data sets. Genome researchers are using cluster analysis to find meaningful groups in microarray data. However, the high dimensionality of the data sets hinders users from finding interesting patterns, clusters, and outliers. Determining the biological significance of such features remains problematic due to the difficulties of integrating biological knowledge. In addition, it is not efficient to perform a cluster analysis over the whole data set in cases where researchers know the approximate temporal pattern of the gene expression that they are seeking. To address these problems, we developed the Hierarchical Clustering Explorer 2.0 by adding three new features to HCE:

If you have any comment or question, send an email to Jinwook Seo (jinwook@cs.umd.edu).

Current version of HCE is downloadable from this page. [download]

Scatterplot Ordering

The large number of possible scatterplots for a high dimensional data set can present a problem, so users need efficient mechanisms to investigate the possible scatterplots. HCE 2.0 provides users with five meaningful criteria to order 2D projections. The first three criteria are useful to reveal statistical relationships between two experimental conditions (or samples), and the next two are useful to find projections of interesting distributions:

Data displayed is from a cDNA microarray experiment data set (31 melanoma + 7 controls) by Bittner

Gene Ontology Browser

HCE2.0 combines GO annotation data with clustering results of microarray experiment data sets to present the biological significance of the results in a unified and structured manner.  Since most microarray experiment stations don't produce GO annotation in the output by default, scripts or relational database queries are necessary to add GO annotations to the microarray experiment data.  We join biological databases to get gene ontology identifiers of genes.  For example, we used UniGene and LocusLink to add GO annotation to the melanoma microarray data set (Bittner et al., 2000).  Genes can be compared in terms of up-to-date GO annotations available at the Gene Ontology consortium website.

Data displayed is from a cDNA microarray experiment data set (31 melanoma + 7 controls) by Bittner et al., 2000.

Profile Search

Many microarray experiments measure gene expression over time. Researchers would like to group genes with similar expression profiles or find interesting time-varying patterns in the data set. Often times, they roughly know the time varying patterns that they want to find. For example, they might be interested in the genes that are up-regulated in a certain time and down-regulated in remaining periods. In such cases, researchers might benefit from a query environment where they can easily specify queries, instantly see the result of the queries, and easily modify their queries.

HCE 2.0 provides the Profile Search that allows for rapid creation and modification of desired profiles. Key design concepts are

The data set shown is a temporal gene expression profile on the mouse muscle regeneration (Zhao et al., 2002).


HCE is a standalone Windows® application running on a general PC environment. It is freely downloadable for academic and/or research purposes. Commercial licenses can be negotiated with the UM Office of Technology Commercialization (Gayatri Varma, gayatri@umd.edu).

Register and Download HCE version 2.0 beta now!

User's Guide for HCE version 2.0 beta

Check whether there is a newer version (go to the Download section at the main project page).

System requirements
Intel® Pentium® processor
Microsoft® Windows 2000®, Windows XP

Last updated 11/19/2004

Web Accessibility