Title
Clustering Gene-Expression Data With Repeated Measurements
Publication Date
4-25-2003
Document Type
Article
Abstract
Clustering is a common methodology for the analysis of array data, and many research laboratories are generating array data with repeated measurements. We evaluated several clustering algorithms that incorporate repeated measurements, and show that algorithms that take advantage of repeated measurements yield more accurate and more stable clusters. In particular, we show that the infinite mixture model-based approach with a built-in error model produces superior results.
Publication Title
Genome Biology
Volume
4
Issue
5
DOI
10.1186/gb-2003-4-5-r34
Publisher Policy
open access
Open Access Status
OA Journal
Recommended Citation
Yeung, Ka Yee; Medvedovic, Mario; and Bumgarner, Roger E., "Clustering Gene-Expression Data With Repeated Measurements" (2003). School of Engineering and Technology Publications. 311.
https://digitalcommons.tacoma.uw.edu/tech_pub/311
