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)
``` |

`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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