title: A global × global test for testing associations between two large sets of variables.
abstract: In high-dimensional omics studies where multiple molecular profiles are obtained for each set of patients, there is often interest in identifying complex multivariate associations, for example, copy number regulated expression levels in a certain pathway or in a genomic region. To detect such associations, we present a novel approach to test for association between two sets of variables. Our approach generalizes the global test, which tests for association between a group of covariates and a single univariate response, to allow high-dimensional multivariate response. We apply the method to several simulated datasets as well as two publicly available datasets, where we compare the performance of multivariate global test (G2) with univariate global test. The method is implemented in R and will be available as a part of the globaltest package in R.
authors.affiliation: Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.|Netherlands Bioinformatics Center, Nijmegen, The Netherlands.
authors.forename: Nimisha
authors.initials: N
authors.lastname: Chaturvedi
authors.affiliation: Epidemiology and Biostatistics, VU University Medical Center, Amsterdam, The Netherlands.|Netherlands Bioinformatics Center, Nijmegen, The Netherlands.
authors.forename: Renée X
authors.initials: RX
authors.lastname: de Menezes
authors.affiliation: Biostatistics, Department for Health Evidence, Radboud University Medical Center, Nijmegen, The Netherlands.|Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands.
authors.forename: Jelle J
authors.initials: JJ
authors.lastname: Goeman
mesh_terms: D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling; D023281:Genomics; D006801:Humans; D015999:Multivariate Analysis; D012984:Software
keywords: Global test; Group testing; High-dimensional data; Multivariate analysis