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Bioinformatics Advance Access published online on June 22, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm328
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Multivariate Correlation Estimator for Inferring Functional Relationships from Replicated Genome-wide Data

Dongxiao Zhu a,*, Youjuan Li b and Hua Li a

aStowers Institute for Medical Research, 1000 E 50th St, Kansas City, MO 64110, bDepartment of Statistics, University of Michigan, Ann Arbor, MI 48105

*to whom correspondence should be addressed. Dr. Dongxiao Zhu, E-mail: doz{at}stowers-institute.org


  Abstract

Summary Estimating pairwise correlation from replicated genomescale (a.k.a. OMICS) data is fundamental to cluster functionally relevant biomolecules to a cellular pathway. The popular Pearson correlation coefficient estimates bivariate correlation by averaging over replicates. It is not completely satisfactory since it introduces strong bias while reducing variance. We propose a new multivariate correlation estimator that models all replicates as independent and identically distributed (i.i.d.) samples from the multivariate normal distribution. We derive the estimator by maximizing the likelihood function. For small sample data, we provide a resampling based statistical inference procedure, and for moderate to large sample data, we provide an asymptotic statistical inference procedure based on the Likelihood Ratio Test (LRT). We demonstrate advantages of the new multivariate correlation estimator over Pearson bivariate correlation estimator using simulations and real-world data analysis examples.

Availability: The estimator and statistical inference procedures have been implemented in an R package "CORREP" that is available from CRAN [http://cran.r-project.org] and Bioconductor [http://www.bioconductor.org/].

Associate Editor: Prof. Alfonso Valencia

Received on January 23, 2007; revised on May 31, 2007; accepted on June 17, 2007

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