Bioinformatics Advance Access originally published online on June 22, 2007
Bioinformatics 2007 23(17):2298-2305; doi:10.1093/bioinformatics/btm328
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Multivariate correlation estimator for inferring functional relationships from replicated genome-wide data
1Stowers Institute for Medical Research, 1000 E 50th Street, Kansas City, MO 64110 and 2Department of Statistics, University of Michigan, Ann Arbor, MI 48105, USA
*To whom correspondence should be addressed.
| Abstract |
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Summary: Estimating pairwise correlation from replicated genome-scale (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/].
Contact: doz{at}stowers-institute.org or dongxiaozhu{at}yahoo.com
Supplementary information: Supplementary data are available at Bioinformatics online.
Received on January 23, 2007; revised on May 31, 2007; accepted on June 17, 2007