hic_simulate {HiCcompare} | R Documentation

## Simulate a Hi-C matrix and perform HiCcompare analysis on it

### Description

Simulate a Hi-C matrix and perform HiCcompare analysis on it

### Usage

    hic_simulate(nrow = 100, medianIF = 50000, sdIF = 14000,
      powerlaw.alpha = 1.8, sd.alpha = 1.9, prop.zero.slope = 0.001,
      centromere.location = NA, CNV.location = NA, CNV.proportion = 0.8,
      CNV.multiplier = 0, biasFunc = .normal.bias, fold.change = NA,
      i.range = NA, j.range = NA, Plot = TRUE, scale = TRUE,
      alpha = 0.05, diff.thresh = NA, include.zeros = FALSE)

### Arguments

`nrow` | Number of rows and columns of the full matrix
`medianIF` | The starting value for a power law distribution for the interaction frequency of the matrix. Should use the median value of the IF at distance = 0. Typical values for 1MB data are around 50,000. For 500kb data typical values are 25,000. For 100kb data, 4,000. For 50kb data, 1,800.
`sdIF` | The estimated starting value for a power law distriubtion for the standard deviaton of the IFs. Should use the SD of the IF at distance = 0. Typical value for 1MB data is 19,000.
`powerlaw.alpha` | The exponential parameter for the power law distribution for the median IF. Typical values are 1.6 to 2. Defaults to 1.8.
`sd.alpha` | The exponential parameter for the power law distribution for the SD of the IF. Typical values are 1.8 to 2.2. Defaults to 1.9.
`prop.zero.slope` | The slope to be used for a linear function of the probability of zero in matrix = slope * distance
`centromere.location` | The location for a centromere to be simulated. Should be entered as a vector of 2 numbers; the start column number and end column number. i.e. to put a centromere in a 100x100 matrix starting at column 47 and ending at column 50 enter centromere.location = c(47, 50). Defaults NA indicating no simulated centromere will be added to the matrix.
`CNV.location` | The location for a copy number variance (CNV). Should be entered as a vector of 2 numbers; the start column number and end column number. i.e. to put a CNV in a 100x100 matrix starting at column 1 and ending at column 50 enter CNV.location = c(1, 50). Defaults NA indicating no simulated CNV will be added to the matrices. If a value is entered one of the matrices will have a CNV applied to it.
`CNV.proportion` | The proportion of 0's to be applied to the CNV location specified. Defaults to 0.8.
`CNV.multiplier` | A multiplyer to be applied as the CNV. To approximate deletion set to 0, to increase copy numbers set to a value > 1. Defaults to 0.
`biasFunc` | A function used for adding bias to one of the simulated matrices. Should take an input of unit distance and generally have the form of 1 + Probability Density Function with unit distance as the random variable. Can also use a constant as a scaling factor to add a global offset to one of the matrices. The output of the bias function will be multiplied to the IFs of one matrix. Included are a normal kernel bias and a no bias function. If no function is entered, a normal kernel bias with an additional global scaling factor of 4 will be used. To use no bias set biasFunc = .no.bias, see examples section.
`fold.change` | The fold change you want to introduce for true differences in the simulated matrices. Defaults to NA for no fold change added.
`i.range` | The row numbers for the cells that you want to introduce true differences at. Must be same length as j.range. Defaults to NA for no changes added.
`j.range` | The column numbers for the cells that you want to introduce true differences at. Must be same length as Defaults to NA for no changes added.
`Plot` | Logical, should the HiCdiff plots be output? Defaults to TRUE.
`scale` | Logical, Should scaling be applied for the HiCdiff procedure? Defaults to TRUE.
`alpha` | Type I error rate parameter. At what level should a significant difference be defined. Defaults to 0.05.
`diff.thresh` | Parameter for hic_diff procedure. See ?hic_diff for more help. Defaults to NA.
`include.zeros` | Should partial zero interactions be included? Defaults to FALSE.

### Value

A list containing the true positive rate (TPR), the specificity (SPC), the p-values, the hic.table object, true differences - a data.table of the rows of the hic.table where a true difference was applied, the truth vector - a vector of 0's and 1's where 1 indicates a true difference was applied to that cell, sim.table - the hic.table object for the simulate matrices before hic_loess and hic_compare was run on it.

### Examples

    # simulate two matrices with no fold changes introduced using default values
    sim <- hic_simulate()

    # example of bias functions
    ## the default function used
    .normal.bias = function(distance) {
      (1 + exp(-((distance - 20)^2) / (2*30))) * 4
    }

    ## an additional bias function
    .no.bias = function(distance) {
      1
    }

    # simulate matrices with 200 true differences using no bias
    i.range = sample(1:100, replace=TRUE)
    j.range = sample(1:100, replace=TRUE)
    sim2 <- hic_simulate(nrow=100, biasFunc = .no.bias, fold.change = 5,
                         i.range = i.range, j.range = j.range)

[Package HiCcompare version 1.9.0 Index]