pca.norm: PCA-based normalization of bin counts

Description Usage Arguments Value Author(s)

View source: R/pca.norm.R

Description

Bin counts are normalized by regressing out the effect of the first Principal Components. Beforehands, the average coverage is normalized. Then PC are computed using prcomp function and regressed out using linear regression.

Usage

1
pca.norm(bc.df, nb.pcs = 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.

nb.pcs

the number of Principal Components to include in the regression model.

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 reference samples; principal components

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. 1, 2018, 12:04 p.m.