sqtl.seeker.p: Permuted sQTL seeker
In dgarrimar/sQTLseekeR2: R package for splicing QTL mapping

Description Usage Arguments Details Value Author(s)

sqtl.seeker.p performs the same test as sqtl.seeker between SNPs and relative transcript expression values, prior permutation of individual labels (see Details).

sqtl.seeker.p(
  tre.df,
  genotype.f,
  gene.loc,
  covariates = NULL,
  genic.window = 5000,
  nb.perm.min = 100,
  nb.perm.max = 1000,
  min.nb.ext.scores = 100,
  min.nb.ind.geno = 10,
  verbose = FALSE
)

`tre.df`	a data.frame with transcript relative expression produced by `prepare.trans.exp`. Same as in `sqtl.seeker`.
`genotype.f`	the name of the genotype file. This file needs to be ordered by position, compressed and indexed using `index.genotype` or externally using tabix (samtools). Must have column 'snpId'. Same as in `sqtl.seeker`.
`gene.loc`	a data.frame with the genes location. Columns 'chr', 'start', 'end' and 'geneId' are required. Same as in `sqtl.seeker`.
`covariates`	a data.frame with covariate information per sample (samples x covariates). Rownames should be the sample ids. Covariates can be either `numeric` or `factor`. When provided, they are regressed out before testing the genotype effect. Default is `NULL`.
`genic.window`	the window(bp) around the gene in which the SNPs are tested. Default is 5000 (i.e. 5kb).
`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 lowest observed nominal P-value to allow the computation to stop. Default is 100.
`min.nb.ind.geno`	SNPs with less samples than `min.nb.ind.geno` in any genotype group are filtered out. Default is 10.
`verbose`	Default is `FALSE`.

sqtl.seeker.p implements an adaptive permutation procedure to control for multiple testing (i.e. multiple genetic variants are tested per gene, see also sqtls.p). The outcome of the permutations is then modeled using beta distributions, as in FastQTL (Ongen et al., 2015), allowing to compute an adjusted empirical P-value per gene.

A data.frame with columns:

`geneId`	the gene name.
`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.