Package: metaCCA 1.35.0

Anna Cichonska

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.

Authors:Anna Cichonska <[email protected]>

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metaCCA/json (API)

# Install 'metaCCA' in R:
install.packages('metaCCA', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • N1 - Number of individuals in study 1.
  • N2 - Number of individuals in study 2.
  • S_XX_study1 - Correlations between 10 SNPs corresponding to the population underlying study 1.
  • S_XX_study2 - Correlations between 10 SNPs corresponding to the population underlying study 2.
  • S_XY_full_study1 - Univariate summary statistics of 10 traits across 1000 SNPs (study 1).
  • S_XY_full_study2 - Univariate summary statistics of 10 traits across 1000 SNPs (study 2).
  • S_XY_study1 - Univariate summary statistics of 10 traits across 10 SNPs (study 1).
  • S_XY_study2 - Univariate summary statistics of 10 traits across 10 SNPs (study 2).

On BioConductor:metaCCA-1.35.0(bioc 3.21)metaCCA-1.34.0(bioc 3.20)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

genomewideassociationsnpgeneticsregressionstatisticalmethodsoftware

4.26 score 5 scripts 254 downloads 9 mentions 3 exports 0 dependencies

Last updated 5 months agofrom:e5665b0c5a. Checks:1 OK, 8 WARNING. Indexed: yes.

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Exports:estimateSyymetaCcaGpmetaCcaPlusGp

Dependencies:

metaCCA

Rendered frommetaCCA.Rnwusingknitr::knitron Mar 29 2025.

Last update: 2019-06-23
Started: 2016-02-18