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

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...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Jianchao Yao, Chunqi Chang, Mari L. Salmi, Yeung Sam Hung, Ann E. Loraine, Stanley J. Roux
• Keywords: BMCBI 2008, Correlation Coefficient, Microarray Data, Pearson Correlation Coefficient