either a data frame or the name of a tab-separated file from which copy number data can be read. The rows of the data frame or file should represent the probes. Column 1 must hold numeric or character chromosome numbers, column 2 the numeric local probe positions, and subsequent columns the numeric copy number measurements for two or more samples. The header of copy number columns should give sample IDs.
pos.unit
the unit used to represent the probe positions. Allowed options are "mbp" (mega base pairs), "kbp" (kilo base pairs) or "bp" (base pairs). By default assumed to be "bp".
arms
optional character vector containing chromosome arms (denoted 'p' and 'q') corresponding to the chromosomes and positions found in data. If not specified chromosome arms are found using the built-in genome assembly version determined by assembly.
Y
either a data frame or the name of a tab-separated file containing original copy number data in the case where data contains Winsorized values. If provided, these values are used to calculate the mean of each segment, otherwise the copy number values in data are used. Y must be on the same form as data.
gamma
penalty for each discontinuity in the curve, default is 40.
normalize
a logical value indicating whether each sample's copy number measurements should be scaled by the sample specific residual standard error. Default is TRUE.
w
a numeric vector giving an individual weight to be used for each sample. May be of length 1 if the same weight should be applied for each sample, default is 1 (no weighting).
fast
a logical value indicating whether a fast (not guaranteed to be exact) version should be run on chromosome arms with > 400 probes. Default is TRUE.
assembly
a string specifying which genome assembly version should be applied to determine chromosome arms. Allowed options are "hg19", "hg18", "hg17" and "hg16" (corresponding to the four latest human genome annotations in the UCSC genome browser).
digits
the number of decimals to be applied when reporting results. Default is 4.
return.est
logical value indicating whether a data frame with copy number estimates (multipcf estimates)should be returned along with the segments. Default is FALSE, which means that only segments are returned.
save.res
logical value indicating whether results should be saved in text files, default is FALSE.
file.names
optional character vector of length two giving the name of the files where the multipcf estimates and segments, respectively, should be saved in case save.res = TRUE.
verbose
logical value indicating whether or not to print a progress message each time multipcf analysis is finished for a new chromosome arm.
Details
Piecewise constant curves are simultaneously fitted to the copy number data for several samples as described in the multiPCF algorithm in Nilsen and Liestoel et al. (2012). This implies that break points will be the same for all segmentation curves, but the mean segment values will differ among samples. Segmentation is done separately on each chromosome arm.
Value
If return.est = TRUE a list with the following components:
estimates
a data frame where the first two columns give the chromosome numbers and probe positions, respectively, while subsequent columns give the copy number estimates for each sample. The estimate for a given probe and sample equals the sample mean of the segment where the probe is located.
segments
a data frame describing the segments found in the data. Each row represents a segment, and the first five columns give the chromosome numbers,
arms, local start positions, local end positions, and the number of probes in the segments, respectively. Subsequent columns give the mean segment value for each sample, with sample IDs as column headers.
If return.est = FALSE only the data frame containing the segments is returned.
If save.res = TRUE the results are also saved in text files with names as specified in file.names. If file.names=NULL, a folder named "multipcf_results" is created in the working directory, and the segments and copy number estimates are saved in this folder as tab-separated files named segments.txt and estimates.txt, respectively.
Note
It is usually advisable to Winsorize data before running pcf, see winsorize on this.
The input data must be complete, see imputeMissing for imputation of missing copy number values.
Author(s)
Gro Nilsen, Knut Liestoel
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
imputeMissing, pcf
Examples
#Load lymphoma data:
data(lymphoma)
#Take out a subset of 3 biopsies from the first patient (using subsetData):
sub.lymphoma <- subsetData(lymphoma,sample=1:3)
#Check for missing values in data:
any(is.na(sub.lymphoma))
#FALSE
#First winsorize data to handle outliers:
wins.lymph <- winsorize(sub.lymphoma)
#Run multipcf on subset lymphoma data (using a low gamma because of low-density data)
multi.segments <- multipcf(data=wins.lymph,gamma=12,Y=sub.lymphoma)
Results
R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> library(copynumber)
Loading required package: BiocGenerics
Loading required package: parallel
Attaching package: 'BiocGenerics'
The following objects are masked from 'package:parallel':
clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
clusterExport, clusterMap, parApply, parCapply, parLapply,
parLapplyLB, parRapply, parSapply, parSapplyLB
The following objects are masked from 'package:stats':
IQR, mad, xtabs
The following objects are masked from 'package:base':
Filter, Find, Map, Position, Reduce, anyDuplicated, append,
as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
rbind, rownames, sapply, setdiff, sort, table, tapply, union,
unique, unsplit
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/copynumber/multipcf.rd_%03d_medium.png", width=480, height=480)
> ### Name: multipcf
> ### Title: Multi-sample copy number segmentation.
> ### Aliases: multipcf
>
> ### ** Examples
>
> #Load lymphoma data:
> data(lymphoma)
>
> #Take out a subset of 3 biopsies from the first patient (using subsetData):
> sub.lymphoma <- subsetData(lymphoma,sample=1:3)
>
> #Check for missing values in data:
> any(is.na(sub.lymphoma))
[1] FALSE
> #FALSE
>
> #First winsorize data to handle outliers:
> wins.lymph <- winsorize(sub.lymphoma)
winsorize finished for chromosome arm 1p
winsorize finished for chromosome arm 1q
winsorize finished for chromosome arm 2p
winsorize finished for chromosome arm 2q
winsorize finished for chromosome arm 3p
winsorize finished for chromosome arm 3q
winsorize finished for chromosome arm 4p
winsorize finished for chromosome arm 4q
winsorize finished for chromosome arm 5p
winsorize finished for chromosome arm 5q
winsorize finished for chromosome arm 6p
winsorize finished for chromosome arm 6q
winsorize finished for chromosome arm 7p
winsorize finished for chromosome arm 7q
winsorize finished for chromosome arm 8p
winsorize finished for chromosome arm 8q
winsorize finished for chromosome arm 9p
winsorize finished for chromosome arm 9q
winsorize finished for chromosome arm 10p
winsorize finished for chromosome arm 10q
winsorize finished for chromosome arm 11p
winsorize finished for chromosome arm 11q
winsorize finished for chromosome arm 12p
winsorize finished for chromosome arm 12q
winsorize finished for chromosome arm 13q
winsorize finished for chromosome arm 14q
winsorize finished for chromosome arm 15q
winsorize finished for chromosome arm 16p
winsorize finished for chromosome arm 16q
winsorize finished for chromosome arm 17p
winsorize finished for chromosome arm 17q
winsorize finished for chromosome arm 18p
winsorize finished for chromosome arm 18q
winsorize finished for chromosome arm 19p
winsorize finished for chromosome arm 19q
winsorize finished for chromosome arm 20p
winsorize finished for chromosome arm 20q
winsorize finished for chromosome arm 21q
winsorize finished for chromosome arm 22q
winsorize finished for chromosome arm 23p
winsorize finished for chromosome arm 23q
>
> #Run multipcf on subset lymphoma data (using a low gamma because of low-density data)
> multi.segments <- multipcf(data=wins.lymph,gamma=12,Y=sub.lymphoma)
multipcf finished for chromosome arm 1p
multipcf finished for chromosome arm 1q
multipcf finished for chromosome arm 2p
multipcf finished for chromosome arm 2q
multipcf finished for chromosome arm 3p
multipcf finished for chromosome arm 3q
multipcf finished for chromosome arm 4p
multipcf finished for chromosome arm 4q
multipcf finished for chromosome arm 5p
multipcf finished for chromosome arm 5q
multipcf finished for chromosome arm 6p
multipcf finished for chromosome arm 6q
multipcf finished for chromosome arm 7p
multipcf finished for chromosome arm 7q
multipcf finished for chromosome arm 8p
multipcf finished for chromosome arm 8q
multipcf finished for chromosome arm 9p
multipcf finished for chromosome arm 9q
multipcf finished for chromosome arm 10p
multipcf finished for chromosome arm 10q
multipcf finished for chromosome arm 11p
multipcf finished for chromosome arm 11q
multipcf finished for chromosome arm 12p
multipcf finished for chromosome arm 12q
multipcf finished for chromosome arm 13q
multipcf finished for chromosome arm 14q
multipcf finished for chromosome arm 15q
multipcf finished for chromosome arm 16p
multipcf finished for chromosome arm 16q
multipcf finished for chromosome arm 17p
multipcf finished for chromosome arm 17q
multipcf finished for chromosome arm 18p
multipcf finished for chromosome arm 18q
multipcf finished for chromosome arm 19p
multipcf finished for chromosome arm 19q
multipcf finished for chromosome arm 20p
multipcf finished for chromosome arm 20q
multipcf finished for chromosome arm 21q
multipcf finished for chromosome arm 22q
multipcf finished for chromosome arm 23p
multipcf finished for chromosome arm 23q
>
>
>
>
>
>
> dev.off()
null device
1
>