Description Usage Arguments Value Examples
View source: R/DNAcopyMethods.R
Detect outliers and smooth the data prior to analysis by programs such as circular binary segmentation (CBS).
1 2 | smooth.CNA(x, smooth.region=10, outlier.SD.scale=4, smooth.SD.scale=2,
trim=0.025)
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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. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 | 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)
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