rolypoly_roll: Main rolypoly wrapper function.
In dcalderon/rolypoly: Identifying Trait-Relevant Functional Annotations

rolypoly_roll

R Documentation

Main rolypoly wrapper function.

Description

The entry point for rolypoly analysis. If no expression data, we assume that we are running just the vegas score processing.

Usage

rolypoly_roll(rolypoly = NULL, gwas_data = NULL, block_annotation = NULL,
  block_data = NULL, ld_folder = NULL, bootstrap_iters = 50,
  outlier_threshold = -1, perform_cv = F, n_folds = 10,
  gwas_z_filter = -1, add_spline = F, n_knots = 1, add_poly = F,
  n_degree = 2, run_light = T, gwas_link_parallel = F,
  bootstrap_parallel = F, keep_model = F, keep_gwas = F, ...)

Arguments

`rolypoly`	Previous rolypoly run to parts of pipeline.
`gwas_data`	Gwas data for a trait, including snp annotations.
`block_annotation`	Start and end points for blocks
`block_data`	Information about blocks.
`ld_folder`	Folder with LD information.
`bootstrap_iters`	Number bootstrap iterations to perform for inference.
`outlier_threshold`	Set to positive if we want to run robusted regression.
`perform_cv`	If we want to interpret annotation effects do not set this to T. However, if our goal is prediction accuracy then set this to T.
`n_folds`	number of folds for cross validation
`gwas_z_filter`	Z-score filter for SNPs, helps prevent large effects biasing inference.
`add_spline`	If we want to fit a spline to maf.
`n_knots`	number of knots to add to the spline.
`add_poly`	If we want to fit a polynomial to maf.
`n_degree`	the degree of the polynomial.
`run_light`	if we want to throw away bootstrap data, and save memory
`gwas_link_parallel`	if user wants to run in gwas linking in parallel, registerDoParallel must have been run in advance.
`bootstrap_parallel`	if user wants to run in bootstraps in parallel, registerDoParallel must have been run in advance.
`keep_model`	if we should keep the regression model, can be large.
`keep_gwas`	set to T if we want to include gwas in returned rolypoly object.
`...`	other arguments to pass to cv.glmnet