pcfPlain: Plain single-sample copy number segmentation.

In igordot/copynumber: Segmentation of single- and multi-track copy number data by penalized least squares regression (with hg38 and mm10).

Description

A basic single-sample pcf segmentation which does not take chromosome borders into account

Usage

pcfPlain(pos.data, kmin = 5, gamma = 40, normalize = TRUE, fast = TRUE, 
      digits = 4, return.est = FALSE, verbose = TRUE)

Arguments

pos.data	a data frame where the rows represent the probes, column 1 holds probe positions, and subsequent column(s) give the numeric copy number measurements for one or more samples. The header of copy number columns should give sample IDs.
kmin	minimum number of probes in each segment, default is 5.
gamma	penalty for each discontinuity in the curve, default is 40.
normalize	logical value indicating whether the copy number measurements should be scaled by the sample residual standard error. Default is TRUE.
fast	a logical value indicating whether a fast (not guaranteed to be exact) version should be run if the number of probes are > 400.
digits	the number of decimals to be applied when reporting results. Default is 4.
return.est	logical value indicating whether a data frame holding copy number estimates (pcf values) should be returned along with the segments. Default is FALSE, which means that only segments are returned.
verbose	logical value indicating whether or not to print a progress message each time pcf analysis is finished for a sample.

Details

A piecewise constant segmentation curve is fitted to the copy number observations as described in the PCF algorithm in Nilsen and Liestoel et al. (2012). Unlike the regular pcf function, pcfPlain does not make independent segmentations for each chromosome arm (i.e. breakpoints are not automatically inserted at the beginning and end of chromosome arms). The segmentation can thus be performed independently of assembly.

Value

If return.est = TRUE a list with the following components:

estimates	a data frame where the first column gives the probe positions, while subsequent column(s) give the copy number estimates for each sample. The estimate for a given probe equals the mean of the segment where the probe is located.
segments	a data frame describing each segment found in the data. Each row represents a segment, while columns give the sampleID, start position, end position, number of probes in the segment and mean value, respectively.

If return.est = FALSE, only the data frame containing the segments is returned.

Note

If probe positions are not available, the first column in data may, e.g., contain the values 1:nrow(data).

Author(s)

Gro Nilsen, Knut Liestoel, Ole Christian Lingjaerde.

References

Nilsen and Liestoel et al., "Copynumber: Efficient algorithms for single- and multi-track copy number segmentation", BMC Genomics 13:591 (2012), doi:10.1186/1471-2164-13-59

See Also

pcf

Examples

#Load the lymphoma data set:
data(lymphoma)

#Take out a smaller subset of 3 samples (using subsetData):
sub.lymphoma <- subsetData(lymphoma,sample=1:3)

#Run pcfPlain (remove first column of chromosome numbers):
plain.segments <- pcfPlain(pos.data=sub.lymphoma[,-1],gamma=12)

igordot/copynumber documentation built on Sept. 18, 2020, 8:48 a.m.