sim.other.methods: Compare other normalization methods on simulated data

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

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)

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