smooth.CNA: Smooth a 'Copy Number Array' data object

In veseshan/DNAcopy: DNA Copy Number Data Analysis

Smooth a ‘Copy Number Array’ data object

Description

Detect outliers and smooth the data prior to analysis by programs such as circular binary segmentation (CBS).

Usage

  smooth.CNA(x, smooth.region=10, outlier.SD.scale=4, smooth.SD.scale=2,
                trim=0.025)

Arguments

x	Copy number array data object
smooth.region	number of points to consider on the left and the right of a point to detect it as an outlier. (default=10)
outlier.SD.scale	the number of SDs away from the nearest point in the smoothing region to call a point an outlier.
smooth.SD.scale	the number of SDs from the median in the smoothing region where a smoothed point is positioned.
trim	proportion of data to be trimmed for variance calculation for smoothing outliers and undoing splits based on SD.

Value

An object of class CNA with outliers smoothed i.e the logratio values of singleton outliers is shrunk towards the values of its neighbors. The output is of the same dimension as the input.

Examples

data(coriell)

#Combine into one CNA object to prepare for analysis on Chromosomes 1-23

CNA.object <- CNA(cbind(coriell$Coriell.05296,coriell$Coriell.13330),
                  coriell$Chromosome,coriell$Position,
                  data.type="logratio",sampleid=c("c05296","c13330"))

#We generally recommend smoothing single point outliers before analysis
#Make sure to check that the smoothing is proper

smoothed.CNA.object <- smooth.CNA(CNA.object)

veseshan/DNAcopy documentation built on Oct. 18, 2023, 8:30 p.m.