Assembles data for all the regions

Usage

assemble_region_data(
  region_info,
  z_snp,
  z_gene,
  weights,
  snp_map,
  thin = 1,
  maxSNP = Inf,
  min_group_size = 100,
  trim_by = c("z", "random"),
  thin_by = c("ref", "gwas"),
  adjust_boundary_genes = TRUE,
  ncore = 1,
  seed = 99,
  logfile = NULL
)

Arguments

region_info: a data frame of region definitions.
z_snp: A data frame with columns: "id", "z", giving the z-scores for SNPs.
z_gene: A data frame with columns: "id", "z", giving the z-scores for genes.
weights: a list of preprocessed weights.
snp_map: a list of data frames with SNP-to-region map for the reference.
thin: The proportion of SNPs to be used for the parameter estimation and initial screening region steps. Smaller thin parameters reduce runtime at the expense of accuracy. The fine mapping step is rerun using full SNPs for regions with strong gene signals.
maxSNP: Inf or integer. Maximum number of SNPs in a region. Default is Inf, no limit. This can be useful if there are many SNPs in a region and you don't have enough memory to run the program.
min_group_size: Minimum number of genes for a group to be included.
trim_by: remove SNPs if the total number of SNPs exceeds maxSNP, options: "z" (trim SNPs with lower |z|), "random".
thin_by: options for thinning SNPs, "reference": thin reference SNPs, "gwas": thin GWAS SNPs.
adjust_boundary_genes: If TRUE, identify boundary genes, and adjust region_data for boundary genes.
ncore: The number of cores used to parallelize susie over regions
seed: seed for random sampling
logfile: path to the log file, if NULL will print log info on screen.

Value

a list of region_data