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

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

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)

HiCcompare documentation built on Nov. 8, 2020, 8:26 p.m.