Bioconductor-mirror/metaCCA: Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis

metaCCA performs multivariate analysis of a single or multiple GWAS based on univariate regression coefficients. It allows multivariate representation of both phenotype and genotype. metaCCA extends the statistical technique of canonical correlation analysis to the setting where original individual-level records are not available, and employs a covariance shrinkage algorithm to achieve robustness.

Getting started

Package details

AuthorAnna Cichonska <[email protected]>
Bioconductor views Genetics GenomeWideAssociation Regression SNP Software StatisticalMethod
MaintainerAnna Cichonska <[email protected]>
LicenseMIT + file LICENSE
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
Bioconductor-mirror/metaCCA documentation built on June 1, 2017, 11:47 a.m.