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

Version

open access

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