Bioinformatics Advance Access originally published online on June 15, 2009
Bioinformatics 2009 25(16):2013-2019; doi:10.1093/bioinformatics/btp357
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
PROMISE: a tool to identify genomic features with a specific biologically interesting pattern of associations with multiple endpoint variables
1Department of Biostatistics, 2Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105, 3Department of Experimental Therapeutics, M.D. Anderson Cancer Center, 1515 Holcombe Blvd., Houston, TX 77030, 4Department of Oncology, 5Department of Pathology, St. Jude Children's Research Hospital, 262 Danny Thomas Place, Memphis, TN 38105 and 6 Department of Experimental and Clinical Pharmacology, University of Minnesota, 308 Harvard St. S.E., Minneapolis, MN 55455, USA
*To whom correspondence should be addressed.
 |
Abstract |
|---|
Motivation: In some applications, prior biological knowledge can be used to define a specific pattern of association of multiple endpoint variables with a genomic variable that is biologically most interesting. However, to our knowledge, there is no statistical procedure designed to detect specific patterns of association with multiple endpoint variables.
Results: Projection onto the most interesting statistical evidence (PROMISE) is proposed as a general procedure to identify genomic variables that exhibit a specific biologically interesting pattern of association with multiple endpoint variables. Biological knowledge of the endpoint variables is used to define a vector that represents the biologically most interesting values for statistics that characterize the associations of the endpoint variables with a genomic variable. A test statistic is defined as the dot-product of the vector of the observed association statistics and the vector of the most interesting values of the association statistics. By definition, this test statistic is proportional to the length of the projection of the observed vector of correlations onto the vector of most interesting associations. Statistical significance is determined via permutation. In simulation studies and an example application, PROMISE shows greater statistical power to identify genes with the interesting pattern of associations than classical multivariate procedures, individual endpoint analyses or listing genes that have the pattern of interest and are significant in more than one individual endpoint analysis.
Availability: Documented R routines are freely available from www.stjuderesearch.org/depts/biostats and will soon be available as a Bioconductor package from www.bioconductor.org.
Contact: stanley.pounds{at}stjude.org
Supplementary information: Supplementary data are available at Bioinformatics online.
Associate Editor: Olga Troyanskaya
Received on January 30, 2009; revised on June 1, 2009; accepted on June 4, 2009
CiteULike 
Connotea 
Del.icio.us What's this?