tmm.norm: Trimmed-Mean M normalization

In jmonlong/PopSV: Population-based detection of structural variants from Read-Depth signal

Description

TMM normalization of bin counts. After such normalization, the majority of bins have the same trimmed average across samples.

Usage

tmm.norm(bc.df, cont.sample, trim.level = 0.3, nb.cores = 1,
  norm.stats.comp = TRUE)

Arguments

bc.df	a data.frame with 'chr', 'start', 'end' columns and then one column per sample with its bin counts.
cont.sample	the sample to use as baseline for the pairwise normalization. All the samples will be normalized to it.
trim.level	How much of the M values should be trimmed (on each side of the distribution). Default is 0.3 (30%).
nb.cores	the number of cores to use. Default is 1.
norm.stats.comp	Should some statistics on the normalized bin count be computed (mean, sd, outliers). Default is TRUE.

Value

a list with

norm.stats	a data.frame witht some metrics about the normalization of each bin (row) : coverage average and standard deviation; number of outlier samples
bc.norm	a data.frame, similar to the input 'bc.df', with the normalized bin counts.

Author(s)

Jean Monlong

jmonlong/PopSV documentation built on Sept. 15, 2019, 9:29 p.m.