peelingOne: A Function to Apply the Peeling Algorithm in a Single Copy...

In DiNAMIC.Duo: Finding Recurrent DNA Copy Number Alterations and Differences

A Function to Apply the Peeling Algorithm in a Single Copy Number Matrix

Description

This function applies the peeling algorithm described in Walter et al. (Bioinformatics, 2011;27(5):678–685)

Usage

peelingOne(X, posDT, k, threshold = NULL)

Arguments

X	A matrix of normalized gene-level copy number data (rows = genes, columns = subjects).
posDT	A data frame containing genomic position information for the genes in X.
k	The location (row of X) containing the peak that will be peeled.
threshold	A tuning parameter that controls the size of the peeled region. Rows of X with mean copy number less than threshold will not be peeled.

Details

to remove a peak from a copy number data set and define a genomic interval of interest around

the peak.

Tumor genomes often contain multiple DNA copy number alterations, e.g., amplifications or deletions. The locus that harbors the most extreme alteration, k, as evidenced by the maximum or minimum column mean, provides a point estimate for the location of an underlying driver gene. Also, loci near k may be affected by the same underlying genomic event. The peeling procedure is applied to "nullify" entries in X that contribute to the alteration at k, thus making it possible to identify altered regions elsewhere in the genome. This function is called by peelingOneIterate.

Value

A list containing two elements: X and interval. X is an updated version of the input copy number matrix in which the peak at k has been removed, and interval is genomic region containing k. By construction, interval cannot extend beyond the chromosome arm containing k.

Examples

lusc=pD[["X"]]

 posDT=pD[["posDT"]]

 kLusc=which.max(rowMeans(lusc))

 peeledLusc=peelingOne(X=lusc,posDT=posDT,k=kLusc,threshold=NULL)   

DiNAMIC.Duo documentation built on March 7, 2023, 8:38 p.m.