sim_matrix | R Documentation |

Simulate 2 Hi-C matrices with differences

```
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
)
```

`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. |

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

```
# 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)
```

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