# sim_matrix: Simulate 2 Hi-C matrices with differences In dozmorovlab/HiCcompare: HiCcompare: Joint normalization and comparative analysis of multiple Hi-C datasets

 sim_matrix R Documentation

## 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
}

.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 Nov. 29, 2022, 9:58 a.m.