epi_quantile: Identifies epimutations using quantile distribution

In isglobal-brge/EpiMutations: Robust outlier identification for DNA methylation data

Identifies epimutations using quantile distribution

Description

Identifies CpGs with outlier methylation values using a sliding window approach to compare individual methylation profiles of a single case sample against all other samples from reference panel (controls)

Usage

epi_quantile(
  case,
  fd,
  bctr_pmin,
  bctr_pmax,
  controls,
  betas,
  window_sz = 1000,
  N = 3,
  offset_abs = 0.15
)

Arguments

case	beta values for a single case (data.frame). The samples as single column and CpGs in rows (named).
fd	feature description as data.frame having at least chromosome and position as columns and and CpGs in rows (named).
bctr_pmin	Beta value observed at 0.01 quantile in controls. A beta values has to be lower or equal to this value to be considered an epimutation.
bctr_pmax	Beta value observed at 0.99 quantile in controls. A beta values has to be higher or equal to this value to be considered an epimutation.
controls	control samples names.
betas	a matrix containing the beta values for all samples.
window_sz	Maximum distance between a pair of CpGs to defined an region of CpGs as epimutation (default: 1000).
N	Minimum number of CpGs, separated in a maximum of window_sz bass, to defined an epimutation (default: 3).
offset_abs	Extra enforcement defining an epimutation based on beta values at 0.005 and 0.995 quantiles (default: 0.15).

Value

The function returns a data frame with the regions candidates to be epimutations.

isglobal-brge/EpiMutations documentation built on April 20, 2024, 9:05 a.m.