compute.empirical.pv: Compute empirical P-value
In guigolab/sQTLseekeR2: R package for splicing QTL mapping

Description Usage Arguments Value Author(s)

Computes empirical P-value for a gene.

compute.empirical.pv(
  genotype.gene,
  tre.mt,
  best.snp,
  min.pv.obs,
  nb.perm.min = 100,
  nb.perm.max = 1000,
  min.nb.ext.scores = 100,
  comp.ld = TRUE,
  verbose = FALSE
)

`genotype.gene`	a data.frame of genotypes produced by `read.bedix`.
`tre.mt`	a matrix of splicing ratios (samples x transcripts).
`best.snp`	SNP with the smallest observed nominal P-value, computed by `compute.nominal.pv`.
`min.pv.obs`	smallest observed nominal P-value.
`nb.perm.min`	the minimum number of permutations. Default is 100.
`nb.perm.max`	the maximum number of permutations. Default is 1000.
`min.nb.ext.scores`	the minimum number of permuted nominal P-values lower than the smallest observed nominal P-value to allow the computation to stop. Default is 100.
`comp.ld`	should linkage disequilibrium estimates be computed (median r2). Default is `TRUE`.
`verbose`	Default is `FALSE`.

a data.frame with columns:

`variants.cis`	the number of variants tested in cis.
`LD`	a linkage disequilibrium estimate for the genomic window (median r2).
`best.snp`	ID of the SNP with the smallest observed nominal P-value.
`best.nominal.pv`	P-value corresponding to the best SNP.
`shape1`	Beta distribution parameter shape1.
`shape2`	Beta distribution parameter shape2.
`nb.perms`	the number of permutations used for the empirical P-value computation.
`pv.emp`	empirical P-value based on permutations.
`pv.emp.beta`	empirical P-value based on the beta approximation.
`runtime`	approximated computation time per gene (mins).