TimeSearcher: Interactive Querying for Identification of Patterns in Genetic Data

Microarray experiments are often used to examine changes in gene expression over time. Generally, these data sets are analyzed using clusters, self-organizing maps, heat maps, and other standard microarray analysis tools. TimeSearcher is a general purpose tool for exploration and pattern identification in time series data. TimeSearcher is based on the use of timeboxes - rectangular, direct-manipulation queries - to support interactive exploration via dynamic queries (100ms response time). TimeSearcher also provides overviews of query results and drag-and-drop support for query-by-example. http://www.cs.umd.edu/hcil/timesearcher

Understanding Hierarchical Clustering Results by Interactive Exploration of Dendrograms: A Case Study with Genomic Microarray

Hierarchical clustering is widely used to find patterns in multi-dimensional datasets, especially for genomic microarray data. Finding groups of genes with similar expression patterns can lead to better understanding of the functions of genes. Current visualization tools for hierarchical clustering that provide static outputs on screens or even large printouts can be improved by adding interactive exploration tools. HCE (Hierarchical Clustering Explorer) is a visualization tool that integrates four general techniques that could be used in interactive explorations of hierarchical clustering results:

  1. overview of the entire dataset, coupled with a detail view so that high-level patterns and hot spots can be easily found and examined,
  2. dynamic query controls so that users can restrict the number of clusters they view at a time and show those clusters more clearly,
  3. coordinated displays: the overview mosaic has a bi-directional link to 2-dimensional scattergrams,
  4. cluster comparisons to allow researchers to see how different clustering algorithms group the genes. HCE can be used for the clustering results of microarray data, and for any multi-dimensional data.

http://www.cs.umd.edu/hcil/multi-cluster