Package 'DNAcopy'

Title:	DNA Copy Number Data Analysis
Description:	Implements the circular binary segmentation (CBS) algorithm to segment DNA copy number data and identify genomic regions with abnormal copy number.
Authors:	Venkatraman E. Seshan, Adam Olshen
Maintainer:	Venkatraman E. Seshan <[email protected]>
License:	GPL (>= 2)
Version:	1.81.0
Built:	2024-11-29 05:08:26 UTC
Source:	https://github.com/bioc/DNAcopy

Help Index

Create ‘Copy Number Array’ data object
Array CGH data set of Coriell cell lines
Cytogenic band data
Results of segmenting a CNA data object
Binary segmentation of exon data.
Sequential stopping boundary
Additional summary measured for the segments
Plot the data and results from segment of a CNA object
Plot the data and results from segmentation for a single sample
Genome Segmentation Program
p-values for the change-points
Additional summary measured for the segments
Smooth a ‘Copy Number Array’ data object
Subset a ‘Copy Number Array’ data object
Subset a DNAcopy data object
Zoomed in view of genomic region

Create ‘Copy Number Array’ data object

Description

Creates a ‘copy number array’ data object used for DNA copy number analyses by programs such as circular binary segmentation (CBS).

Usage

  CNA(genomdat, chrom, maploc, data.type=c("logratio","binary"),
                 sampleid=NULL, presorted = FALSE)
  ## S3 method for class 'CNA'
print(x, ...)
CNA(genomdat, chrom, maploc, data.type=c("logratio","binary"),
                 sampleid=NULL, presorted = FALSE)
  ## S3 method for class 'CNA'
print(x, ...)

Arguments

`genomdat`	a vector or matrix of data from array-CGH, ROMA, or other copy number experiments. If it is a matrix the rows correspond to the markers and the columns to the samples.
`chrom`	the chromosomes (or other group identifier) from which the markers came. Vector of length same as the number of rows of genomdat. If one wants the chromosomes to be ordered in the natural order, this variable should be numeric or ordered category.
`maploc`	the locations of marker on the genome. Vector of length same as the number of rows of genomdat. This has to be numeric.
`data.type`	logratio (aCGH, ROMA, etc.) or binary (LOH).
`sampleid`	sample identifier. If missing the samples are named by prefixing "Sample" to consecutive integers.
`presorted`	logical indicator telling if the data have already been sorted by chrom and maploc. Default is FALSE.
`x`	object returned by CNA
`...`	arguments to be passed onto print command called within.

Details

Data that are NA, Inf, NaN will be removed on a per sample basis for "genomdat" and all samples for "chrom" and "maploc".

If the chrom variable has non-numeric values make it into an ordered variable to get them ordered correctly. E.g. for human genome use: chrom <- ordered(chrom, levels=c(1:22,"X","Y")) to prepare the variable if chromosomes X and Y are present in your data.

Value

An object of class CNA. There is a print method that gives the number of samples and probes and the type of data.

Examples


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"))

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"))

Array CGH data set of Coriell cell lines

Description

These are two data array CGH studies sets of Corriel cell lines taken from the reference below.

Usage

data(coriell)data(coriell)

Format

A data frame containing five variables: first is clone name, second is clone chromosome, third is clone position, fourth (sample GM05296 from Web Table G) and fifth (sample GM13330 from Web Table H) are log2ratio for two cell lines.

Source

Supplementary Information from https://www.nature.com/articles/ng754

References

Snijders et al., Assembly of microarrays for genome-wide measurement of DNA copy number, Nature Genetics, 2001

Cytogenic band data

Description

Cytogenic band data from the goldenPath repository

Usage

data(cytoBand)data(cytoBand)

Format

A data frame containing five variables: chromosome, start and end positions, band name and giesma stain.

Source

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/database/cytoBand.txt.gz

Results of segmenting a CNA data object

Description

The results of segmenting data from copy number array experiments from programs such as circular binary segmentation (CBS).

Usage

## S3 method for class 'DNAcopy'
print(x, showSegRows=FALSE, ...)
## S3 method for class 'DNAcopy'
print(x, showSegRows=FALSE, ...)

Arguments

`x`	an object of class `DNAcopy` – output of segment.
`showSegRows`	option to show row numbers for the segment start and end. default is FALSE.
`...`	arguments to be passed onto print command called within.

Details

An object of class DNAcopy. There is a print method that prints the results in a tabular format. Each row gives the sample, the chromosome, the start and end map locations, the number of markers and the mean of each segment.

Value

`data`	The original CNA object which was the input for segment
`ID`	sample identifier.
`chrom`	the chromosome within the sample.
`loc.start`	the starting map location of the segment
`loc.end`	the ending map location of the segment
`num.mark`	the number of markers in the segment
`data.type`	the segment mean.
`call`	the call that produced the object.

Binary segmentation of exon data.

Description

Compute the binary segmentation statistic, location and approximate p-value.

Usage

  exon.segment(gene, eloc, edat, ngrid=100, tol=1e-6)
exon.segment(gene, eloc, edat, ngrid=100, tol=1e-6)

Arguments

`gene`	gene names in the exon data
`eloc`	exon locations within gene
`edat`	exon expressions within gene
`ngrid`	number grid points for the integral
`tol`	tolerance level for calculating nu

Details

The p-values are obtained by applying Siegmund's approximation for the maximal statistic from binary segmenting consecutive segments within a chromosome. These are one-sided test for an increase in expression.

Value

a matrix with three columns. The maximal statistic from binary segmentation, its location and the p-values for each gene.

Author(s)

Venkatraman E. Seshan

Examples


# test code on an easy data set
set.seed(25)
gene <- rep(c("A", "B"), c(30,20))
eloc <- c(1:30, 1:20)
edat <- matrix(rnorm(500), 50, 10)
# changes for gene1 in samples 3 & 7
edat[1:30, 3] <- edat[1:30, 3] + rep(0.9*0:1, c(17, 13))
edat[1:30, 7] <- edat[1:30, 7] + rep(1.1*0:1, c(21, 9))
# changes for gene2 in samples 4 & 7
edat[31:50, 4] <- edat[31:50, 4] + rep(1.1*0:1, c(8, 12))
edat[31:50, 7] <- edat[31:50, 7] + rep(1.2*0:1, c(13, 7))
exon.segment(gene, eloc, edat)

# test code on an easy data set
set.seed(25)
gene <- rep(c("A", "B"), c(30,20))
eloc <- c(1:30, 1:20)
edat <- matrix(rnorm(500), 50, 10)
# changes for gene1 in samples 3 & 7
edat[1:30, 3] <- edat[1:30, 3] + rep(0.9*0:1, c(17, 13))
edat[1:30, 7] <- edat[1:30, 7] + rep(1.1*0:1, c(21, 9))
# changes for gene2 in samples 4 & 7
edat[31:50, 4] <- edat[31:50, 4] + rep(1.1*0:1, c(8, 12))
edat[31:50, 7] <- edat[31:50, 7] + rep(1.2*0:1, c(13, 7))
exon.segment(gene, eloc, edat)

Sequential stopping boundary

Description

Function to compute the sequential boundary for early stopping.

Usage

  getbdry(eta, nperm, max.ones, tol= 1e-2)
getbdry(eta, nperm, max.ones, tol= 1e-2)

Arguments

`eta`	Type I error rate of the boundary.
`nperm`	Number of permutations for the reference distribution.
`max.ones`	maximum number of ones given by "floor(nperm*alpha)+1".
`tol`	tolerance level for the iterations.

Value

A vector integer values of length max.ones*(max.ones+1)/2 corresponding to the boundary for the number of ones from 1 to max.ones. The default boundary for nperm=10000, eta=0.05, alpha=0.01 is stored in the data object "default.DNAcopy.bdry". Use this function to get the boundary for your favorite values for the parameters "nperm, eta, alpha" and use it for the argument "sbdry" in the function "segment."

Additional summary measured for the segments

Description

This program computes the frequency of gains and losses for each probe as a function of level of mad.

Usage

  glFrequency(xout, threshold=1)
glFrequency(xout, threshold=1)

Arguments

`xout`	an object of class DNAcopy
`threshold`	threshold value to call gain or loss

Value

A segment is called a gain or loss if the segment mean is at least the threshold* mad distance away from the median copy number level. The output is a data frame with five columns which give the chromosome (chrom), genomic position (maploc), the number of samples with available data (pfreq), and the gain (gain) and loss (loss).

Author(s)

Venkatraman E. Seshan

Plot the data and results from segment of a CNA object

Description

Plots the data from a copy number array experiment (aCGH, ROMA etc.) along with the results of segmenting it into regions of equal copy numbers.

Usage

  ## S3 method for class 'DNAcopy'
plot(x, plot.type=c("whole", "plateau", "samplebychrom",
               "chrombysample"), xmaploc=FALSE, altcol=TRUE, sbyc.layout=
                NULL, cbys.nchrom=1, cbys.layout=NULL, include.means=TRUE,
                zeroline=TRUE, pt.pch=NULL, pt.cex=NULL, pt.cols=NULL,
                segcol= NULL, zlcol=NULL, ylim=NULL, lwd=NULL, ...)
## S3 method for class 'DNAcopy'
plot(x, plot.type=c("whole", "plateau", "samplebychrom",
               "chrombysample"), xmaploc=FALSE, altcol=TRUE, sbyc.layout=
                NULL, cbys.nchrom=1, cbys.layout=NULL, include.means=TRUE,
                zeroline=TRUE, pt.pch=NULL, pt.cex=NULL, pt.cols=NULL,
                segcol= NULL, zlcol=NULL, ylim=NULL, lwd=NULL, ...)

Arguments

`x`	an object of class `DNAcopy` resulting from analyzing data from copy number array experiments.
`plot.type`	the type of plot.
`xmaploc`	logical flag to indicate that the X axis is the maploc position rather than the index. Since the segments are rearranged the plateau plot does not use maploc position.
`altcol`	logical flag to indicate if chromosomes should be plotted in alternating colors in the whole genome plot.
`sbyc.layout`	`layout` settings for the multifigure grid layout for the ‘samplebychrom’ type. It should be specified as a vector of two integers which are the number of rows and columns. The default values are chosen based on the number of chromosomes to produce a near square graph. For normal genome it is 4x6 (24 chromosomes) plotted by rows.
`cbys.layout`	`layout` settings for the multifigure grid layout for the ‘chrombysample’ type. As above it should be specified as number of rows and columns and the default chosen based on the number of samples.
`cbys.nchrom`	the number of chromosomes per page in the layout. The default is 1.
`include.means`	logical flag to indicate whether segment means are to be drawn.
`zeroline`	logical flag to indicate whether a horizontal line at y=0 is to be drawn.
`pt.pch`	the plotting character used for plotting the log-ratio values (default is ".").
`pt.cex`	the size of plotting character used for the log-ratio values (default is 3).
`pt.cols`	the color list for the points. The colors alternate between chromosomes. If missing the point colors are black and green.
`segcol`	the color of the lines indicating the segment means. If missing the line color is set to be red.
`zlcol`	the color of the zeroline. If missing it is set to be grey.
`ylim`	this argument is present to override the default limits which is the range of symmetrized log-ratios.
`lwd`	line weight of lines for segment mean and zeroline. If missing it is set to 3.
`...`	other arguments which will be passed to `plot` commands.

Details

There are four possible plot types. For the type ‘whole’ the data are plotted for the entire genome. For the ‘samplebychrom’ type a graph with each chromosome (of a given sample) is drawn in a separate figure on a multi-figure grid. For the ‘plateau’ type the graph is drawn with the chromosome segments re-ordered by the segment means. For the ‘chrombysample’ type the samples for a given chromosome are drawn in a 4x6 multi-figure grid in multiples of 24. By default the segments means are drawn. For multisample data each sample or chromosome is drawn on a separate sheet. When invoked interactively the user is prompted before advancing to the next sample.

Examples


#Read in two examples from Snijders et al.

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

#Plot whole studies

plot(segment.smoothed.CNA.object, plot.type="w")

#Plot each study by chromosome

plot(segment.smoothed.CNA.object, plot.type="s")

#Plot each chromosome across studies (6 per page)

plot(segment.smoothed.CNA.object, plot.type="c", cbys.layout=c(2,1), cbys.nchrom=6)

#Plot by plateaus

plot(segment.smoothed.CNA.object, plot.type="p")

#Read in two examples from Snijders et al.

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

#Plot whole studies

plot(segment.smoothed.CNA.object, plot.type="w")

#Plot each study by chromosome

plot(segment.smoothed.CNA.object, plot.type="s")

#Plot each chromosome across studies (6 per page)

plot(segment.smoothed.CNA.object, plot.type="c", cbys.layout=c(2,1), cbys.nchrom=6)

#Plot by plateaus

plot(segment.smoothed.CNA.object, plot.type="p")

Plot the data and results from segmentation for a single sample

Description

Plots the data for a single sample from a copy number array experiment (aCGH, ROMA etc.) along with the results of segmenting it into regions of equal copy numbers.

Usage

  plotSample(x, sampleid=NULL, chromlist=NULL, xmaploc=FALSE,
             col=c("black","green"), pch=".", cex=NULL, altcol=TRUE,
             segcol="red", lwd=3, zeroline=TRUE, zlcol="grey",
             xlab=NULL, ylab=NULL, main=NULL, ...)
plotSample(x, sampleid=NULL, chromlist=NULL, xmaploc=FALSE,
             col=c("black","green"), pch=".", cex=NULL, altcol=TRUE,
             segcol="red", lwd=3, zeroline=TRUE, zlcol="grey",
             xlab=NULL, ylab=NULL, main=NULL, ...)

Arguments

`x`	an object of class `DNAcopy` resulting from analyzing data from copy number array experiments.
`sampleid`	the sample for which the plot is requested. Should be a valid sample name or number. If missing the first sample is plotted.
`chromlist`	a vector of chromosome numers or names to be plotted. If missing the whole genome is plotted.
`xmaploc`	a logical indicating if data are plotted against genomic position or Index. Defaults to `FALSE`.
`col`	a vector of two colors that can be used for alternating colors for successive chromosomes.
`pch`	the plotting character. Defaults to `.`.
`cex`	the size of plotting character. If missing it is set to 3 if pch is ‘.’ and 1 otherwise.
`altcol`	a logical indicating if colors of successive chromosomes should be alternated. Defaults to `TRUE`.
`segcol`	color for segment means.
`zeroline`	a logical indicating if the zeroline is drawn. Defaults to `TRUE`.
`zlcol`	color for zero line.
`lwd`	thickness of the lines.
`xlab`	the x-axis lavel. If missing Index or Genomic Position will be used depending on xmaploc.
`ylab`	the y-axis label. If missing log(CN) or LOH will be used depending on data type.
`main`	the main title. If missing sample name will be used.
`...`	other arguments to the `plot` function can be passed here.

Details

This function plots the whole genome and segmentation results for a single sample. This function overcomes the deficiency in the plot.DNAcopy function which cycles through all the samples. If sampleid is not specified the first sample is plotted.

Examples


#Read in two examples from Snijders et al.

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

# Plot whole sample c13330

plotSample(segment.smoothed.CNA.object, sampleid="c13330")

# Plot only chromosomes 1,3,5,7,9 from first sample
plotSample(segment.smoothed.CNA.object, sampleid=1, chromlist=c(1,3,5,7,9))

#Read in two examples from Snijders et al.

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

# Plot whole sample c13330

plotSample(segment.smoothed.CNA.object, sampleid="c13330")

# Plot only chromosomes 1,3,5,7,9 from first sample
plotSample(segment.smoothed.CNA.object, sampleid=1, chromlist=c(1,3,5,7,9))

Genome Segmentation Program

Description

This program segments DNA copy number data into regions of estimated equal copy number using circular binary segmentation (CBS).

Usage

  segment(x, weights = NULL, alpha = 0.01, nperm = 10000, p.method = 
                    c("hybrid", "perm"), min.width=2, kmax=25, nmin=200, 
                    eta=0.05, sbdry=NULL, trim = 0.025, undo.splits = 
                    c("none", "prune", "sdundo"), undo.prune=0.05, 
                    undo.SD=3, verbose=1)
segment(x, weights = NULL, alpha = 0.01, nperm = 10000, p.method = 
                    c("hybrid", "perm"), min.width=2, kmax=25, nmin=200, 
                    eta=0.05, sbdry=NULL, trim = 0.025, undo.splits = 
                    c("none", "prune", "sdundo"), undo.prune=0.05, 
                    undo.SD=3, verbose=1)

Arguments

`x`	an object of class CNA
`weights`	a vector of weights for the probes. The weights should be inversely proportional to their variances. Currently all weights should be positive i.e. remove probes with zero weight prior to segmentation.
`alpha`	significance levels for the test to accept change-points.
`nperm`	number of permutations used for p-value computation.
`p.method`	method used for p-value computation. For the "perm" method the p-value is based on full permutation. For the "hybrid" method the maximum over the entire region is split into maximum of max over small segments and max over the rest. Approximation is used for the larger segment max. Default is hybrid.
`min.width`	the minimum number of markers for a changed segment. The default is 2 but can be made larger. Maximum possible value is set at 5 since arbitrary widths can have the undesirable effect of incorrect change-points when a true signal of narrow widths exists.
`kmax`	the maximum width of smaller segment for permutation in the hybrid method.
`nmin`	the minimum length of data for which the approximation of maximum statistic is used under the hybrid method. should be larger than 4*kmax
`eta`	the probability to declare a change conditioned on the permuted statistic exceeding the observed statistic exactly j (= 1,...,nperm*alpha) times.
`sbdry`	the sequential boundary used to stop and declare a change. This boundary is a function of nperm, alpha and eta. It can be obtained using the function "getbdry" and used instead of having the "segment" function compute it every time it is called.
`trim`	proportion of data to be trimmed for variance calculation for smoothing outliers and undoing splits based on SD.
`undo.splits`	A character string specifying how change-points are to be undone, if at all. Default is "none". Other choices are "prune", which uses a sum of squares criterion, and "sdundo", which undoes splits that are not at least this many SDs apart.
`undo.prune`	the proportional increase in sum of squares allowed when eliminating splits if undo.splits="prune".
`undo.SD`	the number of SDs between means to keep a split if undo.splits="sdundo".
`verbose`	level of verbosity for monitoring the program's progress where 0 produces no printout, 1 prints the current sample, 2 the current chromosome and 3 the current segment. The default level is 1.

Details

This function implements the cicular binary segmentation (CBS) algorithm of Olshen and Venkatraman (2004). Given a set of genomic data, either continuous or binary, the algorithm recursively splits chromosomes into either two or three subsegments based on a maximum t-statistic. A reference distribution, used to decided whether or not to split, is estimated by permutation. Options are given to eliminate splits when the means of adjacent segments are not sufficiently far apart. Note that after the first split the $\alpha$ -levels of the tests for splitting are not unconditional.

We recommend using one of the undoing options to remove change-points detected due to local trends (see the manuscript below for examples of local trends).

Since the segmentation procedure uses a permutation reference distribution, R commands for setting and saving seeds should be used if the user wishes to reproduce the results.

Data that are NA, Inf, NaN will be removed on a per sample basis for "genomdat" and all samples for "chrom" and "maploc".

Value

An object of class DNAcopy. It has three elements:

`data`	The original CNA object which was the input for segment
`out`	a data frame with six columns. Each row of the data frame contains a segment for which there are six variables: the sample id, the chromosome number, the map position of the start of the segment, the map position of the end of the segment, the number of markers in the segment, and the average value in the segment.
`segRows`	a data frame with the start and end row of each segment in the data matrix. print command shows it with the showSegRows=T
`call`	the call that produced the output object.

Author(s)

Venkatraman E. Seshan [email protected] and Adam Olshen [email protected]

References

Olshen, A. B., Venkatraman, E. S., Lucito, R., Wigler, M. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data. Biostatistics 5: 557-572.

Venkatraman, E. S., Olshen, A. B. (2007) A faster circular binary segmentation algorithm for the analysis of array CGH data. Bioinformatics 23: 657-63.

Examples


# test code on an easy data set
set.seed(25)
genomdat <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(genomdat, chrom, maploc))

# test code on a noisier and hence more difficult data set
set.seed(51)
genomdat <- rnorm(500, sd=0.2) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test2 <- segment(CNA(genomdat, chrom, maploc))

# test code for weighted CBS
set.seed(97)
wts <- sample(1:3, 500, replace=TRUE)
genomdat <- rnorm(500, sd=0.3)/sqrt(wts) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test3 <- segment(CNA(genomdat, chrom, maploc), weights=wts)

#A real analyis

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)
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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

# test code on an easy data set
set.seed(25)
genomdat <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(genomdat, chrom, maploc))

# test code on a noisier and hence more difficult data set
set.seed(51)
genomdat <- rnorm(500, sd=0.2) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test2 <- segment(CNA(genomdat, chrom, maploc))

# test code for weighted CBS
set.seed(97)
wts <- sample(1:3, 500, replace=TRUE)
genomdat <- rnorm(500, sd=0.3)/sqrt(wts) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test3 <- segment(CNA(genomdat, chrom, maploc), weights=wts)

#A real analyis

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)
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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

p-values for the change-points

Description

This program computes pseudo p-values and confidence intervals for the change-points found by the circular binary segmentation (CBS) algorithm.

Usage

  segments.p(x, ngrid=100, tol=1e-6, alpha=0.05, search.range=100, nperm=1000)
segments.p(x, ngrid=100, tol=1e-6, alpha=0.05, search.range=100, nperm=1000)

Arguments

`x`	an object of class DNAcopy
`ngrid`	number grid points for the integral
`tol`	tolerance level for calculating nu
`alpha`	Confidence level is 1-alpha
`search.range`	statistic is maximized over nu +/- search.range
`nperm`	number of permutations for confidence interval

Details

The p-values are obtained by applying Siegmund's approximation for the maximal statistic from binary segmenting consecutive segments within a chromosome. This p-value is only to give the relative importance of the change-points as the CBS is different from the algorithm used here.

The confidence intervals are obtained by a permutation algorithm. The data are permuted to the left and right of the identified change-point and the location of the maximal binary segmentation statistic computed. The confidence interval is given by the quantiles of the permutation distribution of the locations.

The statistical properties of this confidence interval is unknown. It is used to give an idea of the uncertainity on the location of the change-points as the CBS is different from the algorithm used here.

Value

a data frame with ten columns. The maximal statistic from binary segmentation, the p-values and lower and upper alpha/2 confidence limits (as genomic positions) are added to the six columns from the segment command.

NOTE: THE p VALUES ARE APPROXIMATE TAIL PROBABILITIES. ANY VALUE GREATER THAN 0.1 CAN HAVE LARGE ERROR. p > 1 ARE REPLACED WITH 1.

Author(s)

Venkatraman E. Seshan

Examples


# test code on an easy data set
set.seed(25)
genomdat <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(genomdat, chrom, maploc))
segments.p(test1)

# test code on an easy data set
set.seed(25)
genomdat <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
plot(genomdat)
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(genomdat, chrom, maploc))
segments.p(test1)

Additional summary measured for the segments

Description

This program computes the standard deviation, median and the mad of the data for each segment found by the CBS algorithm.

Usage

  segments.summary(x)
segments.summary(x)

Arguments

`x`	an object of class DNAcopy

Value

a data frame with nine columns. The sd, median and mad of each segment is added to the six columns from the segment command.

Author(s)

Venkatraman E. Seshan

Examples


# test code on an easy data set
set.seed(25)
genomdat1 <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
genomdat2 <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
genomdat1[sample(1:500,5)] <- NA
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(cbind(genomdat1,genomdat2), chrom, maploc))
segments.summary(test1)

# test code on an easy data set
set.seed(25)
genomdat1 <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
genomdat2 <- rnorm(500, sd=0.1) +
rep(c(-0.2,0.1,1,-0.5,0.2,-0.5,0.1,-0.2),c(137,87,17,49,29,52,87,42))
genomdat1[sample(1:500,5)] <- NA
chrom <- rep(1:2,c(290,210))
maploc <- c(1:290,1:210)
test1 <- segment(CNA(cbind(genomdat1,genomdat2), chrom, maploc))
segments.summary(test1)

Smooth a ‘Copy Number Array’ data object

Description

Detect outliers and smooth the data prior to analysis by programs such as circular binary segmentation (CBS).

Usage

  smooth.CNA(x, smooth.region=10, outlier.SD.scale=4, smooth.SD.scale=2,
                trim=0.025)
smooth.CNA(x, smooth.region=10, outlier.SD.scale=4, smooth.SD.scale=2,
                trim=0.025)

Arguments

`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.

Value

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.

Examples


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)

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)

Subset a ‘Copy Number Array’ data object

Description

Function to return a subset of a copy number array data object by a list of chromosomes and sample.

Usage

  ## S3 method for class 'CNA'
subset(x, chromlist=NULL, samplelist=NULL, ...)
## S3 method for class 'CNA'
subset(x, chromlist=NULL, samplelist=NULL, ...)

Arguments

`x`	Copy number array data object
`chromlist`	chromosomes of interest. Should be a subset of the valid chromosome names in the original data.
`samplelist`	samples of interest. Can be integers denoting the samples of interest or a vector of valid sample names.
`...`	other arguments which may be passed to `subset`.

Value

An object of class CNA with the data for the list of chromosomes and samples of interest.

Examples


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"))

#Take the first ten chromosomes of the first sample

#subset.CNA.object <- subset.CNA(CNA.object,chromlist=1:10,samplelist="c05296")
subset.CNA.object <- subset(CNA.object,chromlist=1:10,samplelist="c05296")
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"))

#Take the first ten chromosomes of the first sample

#subset.CNA.object <- subset.CNA(CNA.object,chromlist=1:10,samplelist="c05296")
subset.CNA.object <- subset(CNA.object,chromlist=1:10,samplelist="c05296")

Subset a DNAcopy data object

Description

Function to return a subset of a copy number array data object by a list of chromosomes and sample.

Usage

  ## S3 method for class 'DNAcopy'
subset(x, chromlist=NULL, samplelist=NULL, ...)
## S3 method for class 'DNAcopy'
subset(x, chromlist=NULL, samplelist=NULL, ...)

Arguments

`x`	DNAcopy object
`chromlist`	chromosomes of interest. Should be a subset of the valid chromosome names in the original data.
`samplelist`	samples of interest. Can be integers denoting the samples of interest or a vector of valid sample names.
`...`	other arguments which may be passed to `subset`.

Value

An object of class DNAcopy with the input data and the results of segmenting them only for the chromosomes and samples of interest.

Zoomed in view of genomic region

Description

This program computes the frequency of gains and losses for each probe as a function of level of mad.

Usage

  zoomIntoRegion(x, chrom, sampleid, maploc.start=NULL, maploc.end=NULL,
      pt.pch=NULL, pt.cex=NULL, pt.col=NULL, segcol=NULL, seglwd=NULL,
      main=NULL, xlab=NULL, ylab=NULL, ...)
zoomIntoRegion(x, chrom, sampleid, maploc.start=NULL, maploc.end=NULL,
      pt.pch=NULL, pt.cex=NULL, pt.col=NULL, segcol=NULL, seglwd=NULL,
      main=NULL, xlab=NULL, ylab=NULL, ...)

Arguments

`x`	an object of class DNAcopy.
`chrom`	the chromosome in which the region lies.
`sampleid`	the sample of interest.
`maploc.start`	genomic start position of the region of interest. Default is the beginning of the chromosome.
`maploc.end`	genomic end position of the region of interest. Default is the end of the chromosome.
`pt.pch`	the plotting character used for plotting the log-ratio values (default is ".").
`pt.cex`	the size of plotting character used for the log-ratio values (default is 3 if "." and 1 otherwise).
`pt.col`	the color used for the points. Default is green3.
`segcol`	the color of the lines indicating the segment means. If missing the line color is set to be red.
`seglwd`	line weight of lines for segment mean and zeroline. If missing it is set to 3.
`main`	figure title. If missing will be generated by pasting the chromosome, range and sample name together.
`xlab`	x-axis label. If missing "Genomic position" will be used
`ylab`	y-axis label. If missing "log-ratio" will be used
`...`	additional plotting options.

Details

This command plots the region of interest with the log-ratio and segments. It works for a region from a single chromosome in a single sample. So if more than one chromosome and/or one sample are given only the first chromosome from the first sample will be used.

Author(s)

Venkatraman E. Seshan [email protected]

Examples

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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

zoomIntoRegion(segment.smoothed.CNA.object, chrom=10, sampleid="c05296")
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)

#Segmentation at default parameters

segment.smoothed.CNA.object <- segment(smoothed.CNA.object, verbose=1)

zoomIntoRegion(segment.smoothed.CNA.object, chrom=10, sampleid="c05296")