Simulated dataset of Hellinger divergences used in the examples

Source: R/data_HD.R

Each individuals sample includes 10000 cytosine positions

HD

Format

'hdiv is an object from class 'InfDiv' with GRanges-class as elements carrying in the meta-columns the following variables:

c1:

Methylated read counts from the reference sample.

t1:

Unmethylated read counts from the reference sample.

c2:

Methylated read counts from the treatment sample.

t2:

Unmethylated read counts from the treatment sample.

p1:

methylation level from the reference sample.

p2:

methylation level from the treatment sample.

TV:

the total variation distance (difference of methylation levels)

bay.TV:

TV computes with Bayesian correction for the methelation levels.

hdiv:

Hellinger divergence.

jdiv:

J divergence.

'HD' was obtained with function estimateDivergence.

Examples

## Load a dataset of simulated read counts.
data("HD", package = "MethylIT")

## Number of positions with Hellinger divergence values greater than
## the HD_95%
critical.val <- do.call(rbind, lapply(HD, function(x) {
  hd.95 = quantile(x$hdiv, 0.95)
  tv.95 = quantile(abs(x$bay.TV), 0.95)
  return(c(tv = tv.95, hd = hd.95, num.sites.hd95 = sum(x$hdiv > hd.95),
           num.sites.tv95 = sum(x$bay.TV > tv.95)))}))
critical.val
#>       tv.95%    hd.95% num.sites.hd95 num.sites.tv95
#> C1 0.6768846  67.38851            340            336
#> C2 0.6733871  66.11162            339            335
#> C3 0.6678419  64.87521            341            338
#> T1 0.9200523 130.33562            415            415
#> T2 0.9319056 138.29043            413            413
#> T3 0.9341986 138.70708            413            415