genbin-package | R Documentation |
Conveniently run genetics analysis involving external binary executables from R.
Pipelines support simple procedures for creating, reading, and cleaning up files, in some cases complemented by the genio
package.
Maintainer: Alejandro Ochoa alejandro.ochoa@duke.edu (ORCID)
Useful links:
## Not run:
library(genbin)
library(genio)
# examples below assume these "plink" files exist:
# name.bed, name.bim, name.fam, name.phen
name <- 'name'
# number of PCs for some examples:
n_pcs <- 10
### GCTA pipeline
# The GCTA examples assume that `gcta64` is a binary in the system's PATH.
# create GRM from plink files
gcta_grm( name )
# optional: read kinship matrix into R
data <- genio::read_grm( name )
kinship <- data$kinship
# perform mixed linear model association, returning table
data <- gcta_mlma( name )
# association p-values
data$p
# optional: calculate PCs (creates eigenvec/eigenval files)
gcta_pca( name, n_pcs = n_pcs )
# read PCs into R
data <- genio::read_eigenvec( name )
# delete eigenvec/eigenval files
delete_files_pca( name )
# cleanup
# delete GRM files
genio::delete_files_grm( name )
# delete association table
delete_files_gcta_mlma( name )
# delete log
delete_files_log( name )
### PLINK PCA pipeline
# The plink examples assume that `plink2` is a binary in the system's PATH.
# calculate PCs (creates eigenvec/eigenval files)
plink_pca( name, n_pcs = n_pcs )
# optional: read PCs into R
data <- genio::read_eigenvec( name )
# perform PCA association, returning table
data <- plink_glm( name, file_covar = paste0( name, '.eigenvec' ) )
# association p-values
data$p
# cleanup
# delete eigenvec/eigenval files
delete_files_pca( name )
# delete association table
delete_files_plink_glm( name )
# delete log
delete_files_log( name )
## End(Not run)
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