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Vol. 9, Issue 11, 1093-1105, November 1999

LETTER
Large-Scale Clustering of cDNA-Fingerprinting Data

Ralf Herwig,1,3 Albert J. Poustka,1 Christine Müller,2 Christof Bull,1 Hans Lehrach,1 and John O'Brien1

1 Max-Planck Institut für Molekulare Genetik, Ihnestrasse 73, D-14195 Berlin, Germany; 2 Institut für Mathematische Stochastik, Georg-August-Universität, D-37083 Göttingen, Germany

Clustering is one of the main mathematical challenges in large-scale gene expression analysis. We describe a clustering procedure based on a sequential k-means algorithm with additional refinements that is able to handle high-throughput data in the order of hundreds of thousands of data items measured on hundreds of variables. The practical motivation for our algorithm is oligonucleotide fingerprinting---a method for simultaneous determination of expression level for every active gene of a specific tissue---although the algorithm can be applied as well to other large-scale projects like EST clustering and qualitative clustering of DNA-chip data. As a pairwise similarity measure between two p-dimensional data points, x and y, we introduce mutual information that can be interpreted as the amount of information about x in y, and vice versa. We show that for our purposes this measure is superior to commonly used metric distances, for example, Euclidean distance. We also introduce a modified version of mutual information as a novel method for validating clustering results when the true clustering is known. The performance of our algorithm with respect to experimental noise is shown by extensive simulation studies. The algorithm is tested on a subset of 2029 cDNA clones coming from 15 different genes from a cDNA library derived from human dendritic cells. Furthermore, the clustering of these 2029 cDNA clones is demonstrated when the entire set of 76,032 cDNA clones is processed.

3 Corresponding author.

9:1093-1105 ©1999 by Cold Spring Harbor Laboratory Press  ISSN 1088-9051/99 $5.00
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