Skip Navigation


Bioinformatics Advance Access originally published online on June 22, 2007
Bioinformatics 2007 23(17):2298-2305; doi:10.1093/bioinformatics/btm328
This Article
Right arrow Full Text Freely available
Right arrow FREE Full Text (Print PDF) Freely available
Right arrow Supplementary data
Right arrow All Versions of this Article:
23/17/2298    most recent
btm328v2
btm328v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in ISI Web of Science
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by Zhu, D.
Right arrow Articles by Li, H.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhu, D.
Right arrow Articles by Li, H.
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 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 1,*, Youjuan Li 2 and Hua Li 1

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

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.

Associate Editor: Golan Yona


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

Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.