fastlo: Perform fast loess normalization on a Hi-C experiment

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

Description

Perform fast loess normalization on a Hi-C experiment

Usage

fastlo(
  hicexp,
  iterations = 3,
  span = 0.7,
  parallel = FALSE,
  verbose = FALSE,
  max.pool = 0.7
)

Arguments

hicexp	A hicexp object
iterations	The number of iterations (cycles) for fastlo to proceed through.
span	The span of loess fitting. Defaults to 0.7. To automatically calculate a span using the GCV set span = NA. However note that setting span = NA will significantly slow down the normalization process.
parallel	Logical. Should parallel processing be used?
verbose	Logical, should messages about the normalization be printed?
max.pool	The proportion of unit distances after which all further distances will be pooled. Distances before this value will be progressively pooled and any distances after this value will be combined into a single pool. Defaults to 0.7. Warning: do not adjust this value from the default unless you are getting errors related to the lfproc function or due to sparsity in fastlo normalization. If these errors occur it is due to either sparsity or low variance and max.pool will need to be lowered; typically to 0.5 or 0.6.

Details

This function performs the fast loess (fastlo) normalization procedure on a hicexp object. the fast linear loess ("fastlo") method of Ballman (2004) that is adapted to Hi-C data on a per-distance basis. To perform "fastlo" on Hi-C data we first split the data into p pooled matrices. The "progressive pooling" is used to split up the Hi-C matrix by unit distance. Fastlo is then performed on the MA plots for each distance pool. See Stansfield et al (2018) for full description.

Value

A hicexp object that is normalized.

Examples

data("hicexp2")
hicexp2 <- fastlo(hicexp2)

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