permutationThresholds: Set smoothed score thresholds based on permutation...

In sarks: Suffix Array Kernel Smoothing for discovery of correlative sequence motifs and multi-motif domains

Description

Calculate thresholds for SArKS k-mer calling from permutation distribution.

Usage

permutationThresholds(filters, permDist, nSigma = 4)

Arguments

filters	output from sarksFilters function indicating what combinations of filter parameters halfWindow, spatialLength, and minGini to use.
permDist	output from permutationDistribution function.
nSigma	number of standard deviations above mean of permutation distribution at which to set threshold for either windowed or spatially-windowed score.

Value

named list with two elements: ‘theta’ containing a data.frame with the threshold information and ‘.java’ containing an R representation of the java object with this information.

References

Wylie, D.C., Hofmann, H.A., and Zemelman, B.V. (2019) SArKS: de novo discovery of gene expression regulatory motif sites and domains by suffix array kernel smoothing, Bioinformatics, Vol. 35(20), 3944-3952

https://academic.oup.com/bioinformatics/article-abstract/35/20/3944/5418797

Examples

data(simulatedSeqs, simulatedScores)
sarks <- Sarks(simulatedSeqs, simulatedScores, 4, 0, 1)
filters <- sarksFilters(halfWindow=4, spatialLength=0, minGini=1.1)
permDist <- permutationDistribution(sarks, 250, filters, seed=123)
thresholds <- permutationThresholds(filters, permDist, nSigma=2.0)

sarks documentation built on Nov. 8, 2020, 6:54 p.m.