Journal
BIOMETRICAL JOURNAL
Volume 59, Issue 1, Pages 145-158Publisher
WILEY
DOI: 10.1002/bimj.201500106
Keywords
Global test; Group testing; High-dimensional data; Multivariate analysis
Funding
- Netherlands Bioinformatics Centre
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available