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Activity Number: 62
Type: Contributed
Date/Time: Sunday, August 6, 2006 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract - #305457
Title: Comparing Distance Measures for Clustering Time-Course Microarray Data
Author(s): Theresa Scharl*+ and Friedrich Leisch
Companies: Vienna University of Technology and University of Munich
Address: Wiedner Haupstrasse 7, Vienna, 1040, Austria
Keywords: microarray data ; cluster analysis ; distance measures ; R
Abstract:

Clustering time-course microarray data is an important tool for finding co-regulated genes and groups of genes with similar temporal or spatial expression patterns. Depending on the distance measure and cluster algorithm used, different kinds of clusters will be found. In a simulation study on various datasets, the influence of cluster algorithm and distance measure used is investigated. The focus is not to find the "best" combination of cluster algorithm and distance measure, but to gain a deeper understanding for what is going on when different methods are used.

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