TexasScholarWorks
    • Login
    • Submit
    View Item 
    •   Repository Home
    • UT Faculty/Researcher Works
    • UT Faculty/Researcher Works
    • View Item
    • Repository Home
    • UT Faculty/Researcher Works
    • UT Faculty/Researcher Works
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Genome-Scale Cluster Analysis of Replicated Microarrays Using Shrinkage Correlation Coefficient

    Thumbnail
    View/Open
    2008_06_Yao.pdf (1.657Mb)
    Date
    2008-06
    Author
    Yao, Jianchao
    Chang, Chunqi
    Salmi, Mari L.
    Hung, Yeung S.
    Loraine, Ann
    Roux, Stanley J.
    Share
     Facebook
     Twitter
     LinkedIn
    Metadata
    Show full item record
    Abstract
    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.
    Department
    Cellular and Molecular Biology
    Subject
    gene-expression profiles
    abscisic-acid
    ceratopteris-richardii
    mixture
    model
    tyrosine dephosphorylation
    germinating spores
    seed dormancy
    patterns
    arabidopsis
    protein
    biochemical research methods
    biotechnology & applied microbiology
    mathematical & computational biology
    URI
    http://hdl.handle.net/2152/43199
    xmlui.dri2xhtml.METS-1.0.item-citation
    Yao, Jianchao, Chunqi Chang, Mari L. Salmi, Yeung S. Hung, Ann Loraine, and Stanley J. Roux. "Genome-scale cluster analysis of replicated microarrays using shrinkage correlation coefficient." BMC bioinformatics, Vol. 9, No. 1 (Jun., 2008): 288.
    Collections
    • UT Faculty/Researcher Works

    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin

     

     

    Browse

    Entire RepositoryCommunities & CollectionsDate IssuedAuthorsTitlesSubjectsDepartmentsThis CollectionDate IssuedAuthorsTitlesSubjectsDepartments

    My Account

    Login

    Statistics

    View Usage Statistics

    Information

    About Contact Policies Getting Started Glossary Help FAQs

    University of Texas at Austin Libraries
    • facebook
    • twitter
    • instagram
    • youtube
    • CONTACT US
    • MAPS & DIRECTIONS
    • JOB OPPORTUNITIES
    • UT Austin Home
    • Emergency Information
    • Site Policies
    • Web Accessibility Policy
    • Web Privacy Policy
    • Adobe Reader
    Subscribe to our NewsletterGive to the Libraries

    © The University of Texas at Austin