addRandomPeaks: Add random near-cis peaks to a simulated 4C-seq sample
In walter-ca/Basic4CSim: Basic4CSim: Simulation of 4C-seq data

Description Usage Arguments Value Author(s) Examples

This helper function adds random regions of higher signal (peaks) in near-cis to a 4C-seq sample. Random noise as well as modifiers for blind fragments can be added. The default shape of peaks resembles a normal distribution, however block shaped peaks can be created as well.

addRandomPeaks(simTable, vpStart, maxVPArea = 1500, sdVP = 10000, 
vpRegionDist = 200000, otherPeaks = 5, rmaxRange = c(500, 1500), 
rsdRange = c(500, 1000), minRandomVPPeak = -200, maxRandomVPPeak = 500, 
chanceRandomVPPeak = 0.9)

`simTable`	virtual simulation fragment library
`vpStart`	viewpoint position
`maxVPArea`	height of viewpoint peak (with possible exception of actual viewpoint fragment)
`sdVP`	width of viewpoint peak
`vpRegionDist`	length of near-cis region where randomly generated peaks are simulated
`otherPeaks`	number of random peaks to generate
`rmaxRange`	minimum and maximum height for random peaks
`rsdRange`	minimum and maximum width for random peaks
`minRandomVPPeak`	minimum for randomly chosen summand to fragment read count (can be negative)
`maxRandomVPPeak`	maximum for randomly chosen summand to fragment read count (can be negative)
`chanceRandomVPPeak`	chance for occurrence of random summands

Data frame with additional random peaks and viewpoint modification

Carolin Walter

    if(interactive()) {
        simTableFile <- system.file("extdata", "simTable_peaks.csv", 
            package="Basic4CSim")
        simTable = read.csv(simTableFile, sep = "\t", header = TRUE)
        vpStart = 69999869
        set.seed(42)
        simTable = addRandomPeaks(simTable, vpStart)
        head(simTable)
    }