Skip to contents

Function to perform SNP-set analysis using an entropy-based bivariate statistic

compute_association.Rd

Compute the association between SNPs in the given SNP-sets and the phenotype.

Usage

compute_association(
  root_file_name,
  pathpathways = paste(dirname(root_file_name), "/pathways/", sep = ""),
  pathwaylistfile = paste(dirname(root_file_name), "/pathwaylist.txt", sep = ""),
  nullhypmsfile = paste(dirname(root_file_name), "/nullhyp_ms.txt", sep = ""),
  nullhyps2file = paste(dirname(root_file_name), "/nullhyp_s2.txt", sep = ""),
  out_selected = paste(dirname(root_file_name), "/selected_snps.txt", sep = ""),
  seed = 0,
  alpha = 0.05,
  B = 1,
  min_snps = 1,
  max_snps = 15000,
  N_cores = 1,
  n_rows = 1,
  verbosity = 0,
  mode = 0,
  association_mode = "MAX"
)

Arguments

root_file_name

The full path to a triplet of plink .bim, .bed and .fam files, without extension.

pathpathways

Directory containing one tab-delimited file per SNP-set. Each file must be named <SNP_SET_NAME>.txt, contain no header, and store exactly 3 columns: SNP identifier, chromosome, and base-pair position. Example row: rs123<TAB>17<TAB>43044295. SNP identifiers must match the SNP IDs reported in the input .bim file.

pathwaylistfile

Tab-delimited file describing which SNP-sets to analyse. It must contain one row per SNP-set, with no header, in the form <SNP_SET_NAME><TAB><NUM_SNPS>, where <SNP_SET_NAME> matches the corresponding filename in pathpathways without the .txt extension.

nullhypmsfile

The name of the file where to read the vector of samples from the statistic MS under the Null hypothesis.

nullhyps2file

The name of the file where to read the vector of samples from the statistic S2 under the Null hypothesis.

out_selected

The name of the file where to write the results.

seed

The seed for the random number generator.

alpha

The desired significance level for the single gene set.

B

The number of bootstrap samples.

min_snps

The minimum number of SNPs in a pathway.

max_snps

The maximum number of SNPs in a pathway.

N_cores

The number of cores to use for parallel computation.

n_rows

The number of bivariate statistic rows to compute in parallel.

verbosity

Integer verbosity level (0 = minimal output, higher values increase logging).

mode

Compatibility parameter. Only 0 (discrete phenotype) is supported.

association_mode

The mode of association. Allowed values are MAX, ALL.

Value

data.frame A data frame containing the results of the pathway analysis for each SNP-set.

Details

Function to perform SNP-set analysis using an entropy-based bivariate statistic. The function reads the SNP-set files from the directory pathpathways and the list of SNP-sets from the file pathwaylistfile. The function also reads the null hypothesis samples from the files nullhypmsfile and nullhyps2file. The function writes the results to the file out_selected.

Examples

if (FALSE) { # \dontrun{
root <- tempfile("synthetic")
synthetic <- nebula::simulate_synthetic_genotype(
    n_snps = 20,
    n_individuals = 20,
    n_causal_snps = 3
)
nebula::save_synthetic_genotype(synthetic, file = root)
nebula::generate_synthetic_snpsets(
    input_file = paste0(root, ".bim"),
    snps_per_set = 5,
    num_sets = 4,
    root_dir = dirname(root)
)
} # }