By Topic

A method for tight clustering: with application to microarray

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Tseng, G.C. ; Dept. of Biostat., Pittsburgh Univ., PA, USA ; Wong, W.H.

In this paper we propose a method for clustering that produces tight and stable clusters without forcing all points into clusters. Many existing clustering algorithms have been applied in microarray data to search for gene clusters with similar expression patterns. However, none has provided a way to deal with an essential feature of array data: many genes are expressed sporadically and do not belong to any of the significant biological functions (clusters) of interest. In fact, most current algorithms aim to assign all genes into clusters. For many biological studies, however, we are mainly interested in the most informative, tight and stable clusters with sizes of, say, 20-60 genes for farther investigation. Tight Clustering has been developed specifically to address this problem. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling. We validated this method in the expression profiles of the Drosophila life cycle. The result is shown to better serve biological needs in microarray analysis.

Published in:

Bioinformatics Conference, 2003. CSB 2003. Proceedings of the 2003 IEEE

Date of Conference:

11-14 Aug. 2003