sim.other.methods {HiCcompare} | R Documentation

## Compare other normalization methods on simulated data

### Description

Compare other normalization methods on simulated data

### Usage

    sim.other.methods(sim.table, i.range, j.range, Plot = TRUE,
      alpha = 0.05, diff.thresh = NA)

### Arguments

`sim.table` | the sim.table object output from hic_simulate
`i.range` | The row numbers for the cells that you want to introduce true differences at. Must be same length as j.range.
`j.range` | The column numbers for the cells that you want to introduce true differences at. Must be same length as i.range.
`Plot` | Logical, should the HiCdiff plots be output? 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 details.

### 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.

### Examples

    i.range = sample(1:100, replace=TRUE)
    j.range = sample(1:100, replace=TRUE)
    sim <- hic_simulate(i.range = i.range, j.range = j.range, fold.change = 2)
    mat1 <- sim$sim.table[, c('start1', 'start2', 'IF1'), with=FALSE]
    mat2 <- sim$sim.table[, c('start1', 'start2', 'IF2'), with=FALSE]
    mat1 <- sparse2full(mat1) %>% KRnorm
    mat2 <- sparse2full(mat2) %>% KRnorm
    colnames(mat1) <- 1:ncol(mat1)
    colnames(mat2) <-1:ncol(mat2)
    mat1 <- full2sparse(mat1)
    mat2 <- full2sparse(mat2)
    new.tab <- create.hic.table(mat1, mat2, chr= 'chrsim')
    sim2 <- sim.other.methods(new.tab, i.range = i.range , j.range = j.range)

[Package HiCcompare version 1.9.0 Index]