sim_matrix: Simulate 2 Hi-C matrices with differences

In dozmorovlab/HiCcompare: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets

Simulate 2 Hi-C matrices with differences

Description

Simulate 2 Hi-C matrices with differences

Usage

sim_matrix(
  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
)

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.

Value

A hic.table object containing simulated Hi-C matrices.

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)

dozmorovlab/HiCcompare documentation built on June 30, 2023, 3:09 a.m.