metaCCA

Summary Statistics-Based Multivariate Meta-Analysis of Genome-Wide Association Studies Using Canonical Correlation Analysis


Bioconductor version: Release (3.19)

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.

Author: Anna Cichonska <anna.cichonska at gmail.com>

Maintainer: Anna Cichonska <anna.cichonska at gmail.com>

Citation (from within R, enter citation("metaCCA")):

Installation

To install this package, start R (version "4.4") and enter:


if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("metaCCA")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("metaCCA")
metaCCA PDF R Script
Reference Manual PDF
LICENSE Text

Details

biocViews Genetics, GenomeWideAssociation, Regression, SNP, Software, StatisticalMethod
Version 1.32.0
In Bioconductor since BioC 3.3 (R-3.3) (8.5 years)
License MIT + file LICENSE
Depends
Imports
System Requirements
URL https://doi.org/10.1093/bioinformatics/btw052
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Package Archives

Follow Installation instructions to use this package in your R session.

Source Package metaCCA_1.32.0.tar.gz
Windows Binary (x86_64) metaCCA_1.32.0.zip
macOS Binary (x86_64) metaCCA_1.32.0.tgz
macOS Binary (arm64) metaCCA_1.32.0.tgz
Source Repository git clone https://git.bioconductor.org/packages/metaCCA
Source Repository (Developer Access) git clone git@git.bioconductor.org:packages/metaCCA
Bioc Package Browser https://code.bioconductor.org/browse/metaCCA/
Package Short Url https://bioconductor.org/packages/metaCCA/
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