plotSimulatedThresholds: Plots simulation data for QTLseq analysis

In bmansfeld/QTLseqr: QTL mapping using Bulk Segregant Analysis of Next Generation Sequencing data.

Description

as described in Takagi et al., (2013). Genotypes are randomly assigned for each indvidual in the bulk, based on the population structure. The total alternative allele frequency in each bulk is calculated at each depth used to simulate delta SNP-indeces, with a user defined number of bootstrapped replication. The requested confidence intervals are then calculated from the bootstraps. This function plots the simulated confidence intervals by the read depth.

Usage

plotSimulatedThresholds(SNPset = NULL, popStruc = "F2", bulkSize,
  depth = NULL, replications = 10000, filter = 0.3,
  intervals = c(95, 99))

Arguments

SNPset	optional. Either supply your data set to extract read depths from or supply depth vector.
popStruc	the population structure. Defaults to "F2" and assumes "RIL" otherwise.
bulkSize	non-negative integer. The number of individuals in each bulk
depth	optional integer vector. A read depth for which to replicate SNP-index calls. If read depth is defined SNPset will be ignored.
replications	integer. The number of bootstrap replications.
filter	numeric. An optional minimum SNP-index filter
intervals	numeric vector. Confidence intervals supplied as two-sided percentiles. i.e. If intervals = '95' will return the two sided 95% confidence interval, 2.5% on each side.

Value

Plots a deltaSNP by depth plot. Helps if the user wants to know the the delta SNP index needed to pass a certain CI at a specified depth.

Examples

plotSimulatedThresholds <- function(SNPset = NULL, popStruc = "F2", bulkSize = 25,   depth = 1:150, replications = 10000, filter = 0.3, intervals = c(95, 99))

bmansfeld/QTLseqr documentation built on Jan. 24, 2020, 3:56 p.m.