peelingOneIterate: 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 iteratively applies the peelingOne function, thereby identifying multiple

Usage

peelingOneIterate(
  X,
  posDT,
  gain = TRUE,
  nullDist = NULL,
  threshold = NULL,
  numIters = 5
)

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.
gain	A logical value indicating whether gains (TRUE) or losses (FALSE) will be peeled. Default = TRUE.
nullDist	An empirical null distribution produced by the cyclic shift algorithm. Default = NULL.
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. Default = NULL.
numIters	The number of times peelingOne will be iterated. Default = 5.

Details

peaks across the genome in a single cohort. Gains and losses should be analyzed separately.

The peelingOne function applies the peeling algorithm described by Walter et al. (Bioinformatics, 2011;27(5):678–685) at a given marker k. Because tumor genomes may contain multiple regions of copy number gain or loss, it important to apply the peeling process iteratively, thereby identifying multiple markers and surrounding genomic regions.

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"]]

 gain = TRUE

 nullDist = NULL

 threshold = NULL

 numIters = 3

 peeledLusc=peelingOneIterate(X=lusc,posDT=posDT,gain=TRUE,nullDist=NULL,threshold=NULL,numIters=3)   

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