Create Copy Number Array' data object ## Description Creates acopy 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)
  list(list("print"), list("CNA"))(x, list())

Arguments

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"))

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

list(list("print"), list("DNAcopy"))(x, showSegRows=FALSE, list())

Arguments

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

*

Internal DNAcopy functions

Description

Internal functions of package DNAcopy.

Usage

changepoints(genomdat, data.type = "logratio", alpha = 0.01, weights = NULL,
                 sbdry, sbn, nperm = 10000, p.method="hybrid", min.width=2,
                 kmax=25, nmin = 200, trimmed.SD = NULL, undo.splits = "none",
                 undo.prune = 0.05, undo.SD = 3, verbose = 1, ngrid=100,
                 tol=1e-6) 
changepoints.prune(genomdat, lseg, change.cutoff=0.05)
changepoints.sdundo(genomdat, lseg, trimmed.SD, change.SD=3)
trimmed.variance(genomdat, trim=0.025)
inflfact(trim)
exon.changepoint(exondat, ngrid=100, tol=1e-6)

Details

These are not to be called directly by the user

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.

Format

A data frame containing five variables: first is clone name, second is clone chromosome, third is clone position, fourth and fifth are log2ratio for two cell lines.

Usage

data(coriell)

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

Format

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

Usage

data(cytoBand)

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)

Arguments

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

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)

Sequential stopping boundary

Description

Function to compute the sequential boundary for early stopping.

Usage

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

Arguments

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)

Arguments

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

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

list(list("plot"), list("DNAcopy"))(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

Details

There are four possible plot types. For the type whole' the data are plotted for the entire genome. For thesamplebychrom' 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 thechrombysample' 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")

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, ...)

Arguments

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))

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)

Arguments

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 $lpha$ -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:

*

Author

Venkatraman E. Seshan seshanv@mskcc.org and Adam Olshen olshena@biostat.ucsf.edu

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)

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)

Arguments

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

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)

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)

Arguments

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

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)

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 ```r smooth.CNA(x, smooth.region=10, outlier.SD.scale=4, smooth.SD.scale=2, trim=0.025) ``` ## Arguments |Argument |Description| |------------- |----------------| |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 classCNA` 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 r 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 ```r list(list("subset"), list("CNA"))(x, chromlist=NULL, samplelist=NULL, ...) ``` ## Arguments |Argument |Description| |------------- |----------------| |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 tosubset.| ## Value An object of classCNA` with the data for the list of chromosomes and samples of interest. ## Examples r 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

list(list("subset"), list("DNAcopy"))(x, chromlist=NULL, samplelist=NULL, ...)

Arguments

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, ...)

Arguments

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

Venkatraman E. Seshan seshanv@mskcc.org

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")