cyclic_loess: Cyclic Loess normalization for Hi-C data

In multiHiCcompare: Normalize and detect differences between Hi-C datasets when replicates of each experimental condition are available

Description

Cyclic Loess normalization for Hi-C data

Usage

cyclic_loess(
  hicexp,
  iterations = 3,
  span = NA,
  parallel = FALSE,
  verbose = FALSE
)

Arguments

hicexp	A hicexp object
iterations	The number of iterations (cycles) of loess normalization to perform. Defaults to 3.
span	The span for loess normalization. Defaults to NA indicating that span will be automatically calculated using generalized cross validation.
parallel	Logical. Should parallel processing be used?
verbose	Logical. Should messages about loess normalization be printed to the screen.

Details

This function performs cyclic loess normalization on a Hi-C experiment. multiHiCcompare's cyclic loess procedure is a modified version of Ballman's (2004) cyclic loess and the joint loess normalization used in the original HiCcompare. For each unique pair of samples in the hicexp object an MD plot is generated. A loess curve is fit to the MD plot and then the fitted values are used to adjust the data. This is performed on all unique pairs and then repeated until convergence.

Value

A hicexp object that has been normalized.

Examples

#' data("hicexp2")
hicexp2 <- cyclic_loess(hicexp2, span = 0.7)

multiHiCcompare documentation built on Nov. 8, 2020, 7:04 p.m.