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Open Access Highly Accessed Research article

Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient

Jianchao Yao1, Chunqi Chang2, Mari L Salmi3, Yeung Sam Hung2, Ann Loraine4 and Stanley J Roux3*

Author Affiliations

1 Institute for Cellular and Molecular Biology and Department of Mathematics, University of Texas at Austin, Austin, Texas 78712, USA

2 Department of Electrical and Electronic Engineering, University of Hong Kong, Hong Kong, PR China

3 Section of Molecular Cell and Developmental Biology, University of Texas at Austin, Austin, Texas 78712, USA

4 Bioinformatics Research Center, University of North Carolina at Charlotte, Charlotte, NC 28223, USA

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BMC Bioinformatics 2008, 9:288  doi:10.1186/1471-2105-9-288

Published: 18 June 2008

Abstract

Background

Currently, clustering with some form of correlation coefficient as the gene similarity metric has become a popular method for profiling genomic data. The Pearson correlation coefficient and the standard deviation (SD)-weighted correlation coefficient are the two most widely-used correlations as the similarity metrics in clustering microarray data. However, these two correlations are not optimal for analyzing replicated microarray data generated by most laboratories. An effective correlation coefficient is needed to provide statistically sufficient analysis of replicated microarray data.

Results

In this study, we describe a novel correlation coefficient, shrinkage correlation coefficient (SCC), that fully exploits the similarity between the replicated microarray experimental samples. The methodology considers both the number of replicates and the variance within each experimental group in clustering expression data, and provides a robust statistical estimation of the error of replicated microarray data. The value of SCC is revealed by its comparison with two other correlation coefficients that are currently the most widely-used (Pearson correlation coefficient and SD-weighted correlation coefficient) using statistical measures on both synthetic expression data as well as real gene expression data from Saccharomyces cerevisiae. Two leading clustering methods, hierarchical and k-means clustering were applied for the comparison. The comparison indicated that using SCC achieves better clustering performance. Applying SCC-based hierarchical clustering to the replicated microarray data obtained from germinating spores of the fern Ceratopteris richardii, we discovered two clusters of genes with shared expression patterns during spore germination. Functional analysis suggested that some of the genetic mechanisms that control germination in such diverse plant lineages as mosses and angiosperms are also conserved among ferns.

Conclusion

This study shows that SCC is an alternative to the Pearson correlation coefficient and the SD-weighted correlation coefficient, and is particularly useful for clustering replicated microarray data. This computational approach should be generally useful for proteomic data or other high-throughput analysis methodology.