selectSegments: Select multipcf segments

In luciansmith/copynumber: Segmentation of single- and multi-track copy number data by penalized least squares regression.

Description

Selects multipcf segments based on a desired characteristic.

Usage

 selectSegments(segments, what = "variance", thres = NULL, nseg = 10, 
                  large = TRUE, p = 0.1)

Arguments

segments	a data frame containing segments found by multipcf.
what	the desired characteristic to base selection on. Must be one of "variance" (default),"length" and "aberration". See details below.
thres	an optional numeric threshold to be applied in the selection.
nseg	the desired number of segments to be selected, default is 10. Only used if thres=NULL.
large	logical value indicating whether segments with large (TRUE) or small (FALSE) variance, length or mean value should be selected when what is "variance", "length" or "aberration", respectively.
p	a number between 0 and 1 giving the minimum proportion of samples for which an aberration must be detected, default is 0.1. Only applicable if what="aberration".

Details

The input in what determines how the segments are selected. Three options are available:

If what="variance" the variance of the segment values across all samples is calculated for each segment. If thres is specified, the subset of segments for which the variance is above (if large=TRUE) or below (if large=FALSE) the threshold is returned. If thres is not given by the user, a given number of segments determined by the input in nseg is selected; if large=TRUE this will be the nseg segments with the highest variance, whereas if large=FALSE the subset will consist of the nseg segments with the lowest variance.

If what="length" selection is based on the genomic length of the segments (end position minus start position). If thres is specified, the subset of segments for which the length is above (if large=TRUE) or below (if large=FALSE) this threshold is returned. If thres is left unspecified, a given number of segments determined by the input in nseg is selected; if large=TRUE this will be the nseg longest segments, whereas if large=FALSE it will be the nseg shortest segments.

If what="aberration" the aberration frequency is used to select the subset of segments. If thres is specified, the proportion of samples for which the segment value is above (if large=TRUE) or below (if large=FALSE) the threshold is calculated for each segment. The subset of segments where this frequency is above or equal to the proportion set by the parameter p is returned. If thres is not specified, the nseg segments with the highest (1-p)-quantile (if large=TRUE) or the lowest p-quantile (if large=FALSE) is returned.

Value

A list containing:

sel.seg

data frame containing the selected segments.

In addition, depending on the value of what:

seg.var	a vector giving the variance for each segment. Only returned if what = "variance".
seg.length	a vector giving the length of each segment. Only returned if what = "length".
seg.ab.prop	a vector giving the aberration proportion for each segment. Only returned if what = "aberration" and thres is specified.
seg.quantile	a vector giving the (1-p)- or p-quantile for each segment. Only returned if what = "aberration" and thres=NULL.

Author(s)

Gro Nilsen

See Also

multipcf

Examples

#Lymphoma data
data(lymphoma)

#Run multipcf
segments <- multipcf(lymphoma,gamma=12)

#Select the 10 segments with the highest variance:
sel.seg1 <- selectSegments(segments)

#Select the segments where the variance is below 0.001
sel.seg2 <- selectSegments(segments,thres=0.001,large=FALSE)

#Select the 5 longest segments:
sel.seg3 <- selectSegments(segments,what="length",nseg=5)

#Select the segments where 20 % of the samples have segment value of 0.2 or more:
sel.seg4 <- selectSegments(segments,what="aberration",thres=0.2,p=0.2)

#Select the 20 segments with the largest median:
sel.seg5 <- selectSegments(segments,what="aberration",nseg=20,p=0.5)

luciansmith/copynumber documentation built on May 6, 2019, 2:32 p.m.