Pre-process DNA copy number (CN) data for detection of CN events.

Description

Description: The package evaluates DNA copy number data, using both their intitial form (copy number as a noisy function of genomic position) and their approximation by a piecewise-constant function (segmentation), for the purpose of identifying genomic regions where the copy number differs from the norm.

Usage

CNpreprocessing(segall, ratall = NULL, idcol = NULL, startcol = NULL, 
endcol = NULL, medcol = NULL, madcol = NULL, errorcol = NULL, 
chromcol = NULL, bpstartcol = NULL, bpendcol = NULL, annot = NULL, 
annotstartcol = NULL, annotendcol = NULL, annotchromcol = NULL, 
useend = F, blsize = NULL, minjoin = NULL, ntrial = 10, bestbic = -1e+07, 
modelNames = "E", cweight = NULL, bstimes = NULL, chromrange = NULL, 
myseed = 123, distrib = c("vanilla", "Rparallel"), njobs = 1, 
normalength = NULL, normalmedian = NULL, normalmad = NULL, 
normalerror = NULL)

Arguments

Details

Depending on the availability of input, the function will perform the following operations for each copy number profile.

If raw data are available in addition to segment start and end positions, median and MAD of each segment will be computed. For each profile, bootstrap sampling of the segment median values will be performed, and the sample will be used to estimate the error in the median for each segment. Model-dependent clustering (fitting to a gaussian mixture) of the sample will be performed. The central cluster (the one nearest the expected unaltered value) will be identified and, if necessary, merged with adjacent clusters in order to comprise the minimal required fraction of the data. Deviation of each segment from the center, its probabilty to belong to the central cluster and its marginal probability in the central cluster will be computed.

If segment medians or median deviations are available or have been computed, and, in addition, genomic lengths and average values are given for a collection of segments with unaltered copy number, additional estimates will be performed. If median values are available for the unaltered segments, the marginal probability of the observed median or median deviation in the unaltered set will be computed for each segment. Likewise, marginal probabilities for median/MAD and/or median/error will be computed if these statistics are available.

Value

The input segall data frame to which some or all of the following columns may be bound, depending on the availability of input:

Author(s)

Alex Krasnitz

Examples

data(segexample)
data(ratexample)
data(normsegs)
#small toy example
segtable<-CNpreprocessing(segall=segexample[segexample[,"ID"]=="WZ1",],
ratall=ratexample,"ID","start","end",chromcol="chrom",bpstartcol="chrom.pos.start",
bpendcol="chrom.pos.end",blsize=50,minjoin=0.25,cweight=0.4,bstimes=50,
chromrange=1:3,distrib="Rparallel",njobs=2,modelNames="E",
normalength=normsegs[,1],normalmedian=normsegs[,2])
## Not run: 
#Example 1: 5 whole genome analysis, choosing the right format of arguments
segtable<-CNpreprocessing(segall=segexample,ratall=ratexample,"ID","start","end",
chromcol="chrom",bpstartcol="chrom.pos.start",bpendcol="chrom.pos.end",blsize=50,
minjoin=0.25,cweight=0.4,bstimes=50,chromrange=1:22,distrib="Rparallel",njobs=40,
modelNames="E",normalength=normsegs[,1],normalmedian=normsegs[,2])
#Example 2: how to use annotexample, when segment table does not have columns of 
#integer postions in terms of  measuring units(probes), such as "mysegs" below
mysegs<-segexample[,c(1,5:12)]
data(annotexample)
segtable<-CNpreprocessing(segall=mysegs,ratall=ratexample,"ID",chromcol="chrom",
bpstartcol="chrom.pos.start",bpendcol="chrom.pos.end",annot=annotexample,
annotstartcol="CHROM.POS",annotendcol="CHROM.POS",annotchromcol="CHROM",
blsize=50,minjoin=0.25,cweight=0.4,bstimes=50,chromrange=1:22,distrib="Rparallel",
njobs=40,modelNames="E",normalength=normsegs[,1],normalmedian=normsegs[,2])

## End(Not run)

Annotation table for ROMA CGH platform and human genome version 17.

Description

Whole genome annotation table using Representational Oligonucleotide Microarray Analysis (ROMA) CGH platform, human genome version 17.

Usage

data(annotexample)

Format

A data frame with 83055 observations on the following 3 variables.

PROBEID

a character vector

CHROM

a numeric vector

CHROM.POS

a numeric vector

Details

The values in the chromosome column are all integer, with 23 corresponding to X, 24 to Y and 25 to a set of non-human test probes.

Source

GEO accession GPL9775, http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL9775

Examples

data(annotexample)

Apply a mask to a table of copy number events.

Description

A mask is applied to amplified or deleted segments as tabulated in segtable. A decision whether to mask a segment is taken based on what portion of the segment is covered by the mask. A position is chosen at random within a segment to be masked, the flanking segments are extended to that position and the segment to be masked is indicated as such in the value returned.

Usage

applyCNPmask(segtable,chrom,startPos,endPos,startProbe,endProbe,
	eventIndex,masktable,maskchrom,maskstart,maskend,maskindex,mincover=1,
	indexvals=c(-1,1))

Arguments

Details

Masking is performed separately for each value in indexvals. Segments (rows of segtable) with that value of eventIndex are examined for coverage by mask intervals with that value of maskindex in masktable. If the coverage is at least mincover, the segment is slated for masking, while its flanking segments are extended to a random point within the segment being masked.

Value

A matrix with same number of observations/rows as segtable and with following three columns:

Author(s)

Alex Krasnitz

Examples

## Not run: 
data(segexample)
data(ratexample)
data(normsegs)
data(cnpexample)
segtable<-CNpreprocessing(segall=segexample[segexample[,"ID"]=="WZ1",],
ratall=ratexample,"ID","start","end",chromcol="chrom",bpstartcol="chrom.pos.start",
bpendcol="chrom.pos.end",blsize=50,minjoin=0.25,cweight=0.4,bstimes=50,
chromrange=1:22,distrib="Rparallel",njobs=2,modelNames="E",normalength=normsegs[,1],
normalmedian=normsegs[,2])
#form a eventIndex vector
eventIndex<-rep(0,nrow(segtable))
eventIndex[segtable[,"marginalprob"]<1e-4&segtable[,"negtail"]>
0.999&segtable[,"mediandev"]<0] <- -1
eventIndex[segtable[,"marginalprob"]<1e-4&segtable[,"negtail"]>
0.999&segtable[,"mediandev"]>0] <- 1
segtable<-cbind(segtable,eventIndex)
#form a cnpindex vector
namps17<-cnpexample[cnpexample[,"copy.num"]=="amp",]
aCNPmask<-makeCNPmask(imat=namps17,chromcol=2,startcol=3,endcol=4,
nprof=1203,uthresh=0.02,dthresh=0.008)
ndels17<-cnpexample[cnpexample[,"copy.num"]=="del",]
dCNPmask<-makeCNPmask(imat=ndels17,chromcol=2,startcol=3,endcol=4,
nprof=1203,uthresh=0.02,dthresh=0.008)
cnptable<-rbind(cbind(aCNPmask,cnpindex=1),cbind(dCNPmask,cnpindex=-1))
#run the CNP test
myCNPtable<-applyCNPmask(segtable,"chrom",startPos="chrom.pos.start",
endPos="chrom.pos.end","start","end","eventIndex",masktable=cnptable,"chrom",
maskstart="start",maskend="end",maskindex="cnpindex",mincover=0.005,indexvals=c(-1,1))

## End(Not run)

Example of a boundary positions table.

Description

A table of genomic positions for DNA copy-number changing events, collected from genomes of 1203 individuals using Representational Oligonucleotide Microarray Analysis (ROMA) platform.

Usage

data(cnpexample)

Format

A data frame with 19188 rows and 4 columns.

copy.num

a character vector indicating whether an event is a gain ("amp") or a loss ("del").

chrom

a numeric vector indicating which chromosome the event is in.

chrom.start

a numeric vector of event start positions.

chrom.end

a numeric vector of event start positions.

Source

Strong association of de novo copy number mutations with autism. Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J, Spence SJ, Lee AT, Puura K, Lehtimaki T, Ledbetter D, Gregersen PK, Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King MC, Skuse D, Geschwind DH, Gilliam TC, Ye K, Wigler M. Science. 2007 Apr 20;316(5823):445-9.

Examples

data(cnpexample)

Given a set of copy-number events, create a DNA copy-number mask

Description

The function takes as an input a set of intervals with integer-valued boundary positions. It then finds interval regions where the event count is above each of two thresholds, upper and lower, and returns those interval regions with the count above the lower threshold that contain interval regions with the count above the upper threshold.

Usage

makeCNPmask(imat,chromcol=1,startcol=2,endcol=3,nprof=1,uthresh,dthresh)

Arguments

Value

An integer matrix with three columns, called "chrom","start" and "end", specifying the chromosome number and boundary positions of the mask.

Author(s)

Alex Krasnitz, Guoli Sun

Examples

#load a table of copy number events collected from 1203 profiles.
data(cnpexample)
#Create a table of gain (amplification) events only.
amps<-cnpexample[cnpexample[,"copy.num"]=="amp",]
#Create a mask using this table.
ampCNPmask<-makeCNPmask(imat=amps,chromcol="chrom",startcol="chrom.start",
endcol="chrom.end",nprof=1203,uthresh=0.02,dthresh=0.008)

A reference set of segments

Description

A table of segment lengths and log copy number ratios for a large set of human diploid genomes.

Usage

data(normsegs)

Format

A data matrix with 43497 rows/segments and 2 columns/variables.

length

a numeric vector of segment genomic length

segmedian

a numeric vector of segment median computed from log copy number ratio

Details

The table originates in a set of copy number profiles of over a 1000 individuals, obtained using Representational Oligonucleotide Microarray Analysis (ROMA) technology. To ensure ploidy of 2 segments from X and Y chromosomes and segemnts shorter than 5Mb were excluded.

Source

Science. 2007 Apr 20;316(5823):445-9. Epub 2007 Mar 15.

Strong association of de novo copy number mutations with autism.

Sebat J, Lakshmi B, Malhotra D, Troge J, Lese-Martin C, Walsh T, Yamrom B, Yoon S, Krasnitz A, Kendall J, Leotta A, Pai D, Zhang R, Lee YH, Hicks J, Spence SJ, Lee AT, Puura K, Lehtimaki T, Ledbetter D, Gregersen PK, Bregman J, Sutcliffe JS, Jobanputra V, Chung W, Warburton D, King MC, Skuse D, Geschwind DH, Gilliam TC, Ye K, Wigler M.

Examples

data(normsegs)

Example of copy number log ratio data

Description

Log ratio data for 5 breast cancer genomes, derived using Representational Oligonucleotide Microarray Analysis (ROMA), an array-based hybridization method that uses genomic complexity reduction based on representations.

Usage

data(ratexample)

Format

A log ratio data matrix with rows of 83055 oligonucleotide probes, and columns of 5 breast tumors.

Details

The values are natural log copy number ratios, consistent with data in segexample (segmented data for these tumors) and normsegs. These copy number ratios are normalized using an intensity-based lowess curve fitting algorithm.

Source

Genome Res. 2006 Dec;16(12):1465-79.

Novel patterns of genome rearrangement and their association with survival in breast cancer.

Hicks J, Krasnitz A, Lakshmi B, Navin NE, Riggs M, Leibu E, Esposito D, Alexander J, Troge J, Grubor V, Yoon S, Wigler M, Ye K, Borresen-Dale AL, Naume B, Schlicting E, Norton L, Hagerstrom T, Skoog L, Auer G, Maner S, Lundin P, Zetterberg A.

Examples

data(ratexample)
#Plot the whole genome log ratio data for the first profile 
#Note X and Y chromosomes at the far  right of the plot
plot(ratexample[,1])

Example of a segmented copy number table.

Description

Segmented log ratio data for 5 breast cancer genomes, derived using Representational Oligonucleotide Microarray Analysis (ROMA) platform. ROMA detects genomic amplifications and deletions with boundaries defined at a resolution of 50 kb. In this segmented table, each row represnts a segment.

Usage

data(segexample)

Format

A data frame with 479 rows/segments and 12 columns/variables.

ID

a character vector of profile IDs

start

a numeric vector (segment start probe number)

end

a numeric vector (segment end probe number)

num.probes

a numeric vector (number of probes in the segment)

seg.median

a numeric vector (median log ratio)

chrom

a numeric vector (chromosome number)

chrom.pos.start

a numeric vector (genomic start)

chrom.pos.end

a numeric vector (genomic end)

cytoband.start

a character vector (cytogenetic band start)

cytoband.end

a character vector (cytogenetic band end)

abs.pos.start

a numeric vector (genomic start, absolute)

abs.pos.end

a numeric vector (genomic end, absolute)

Details

Segment medians are computed from log copy number ratio. The corresponding raw data table is ratexample in this package.

Source

Genome Res. 2006 Dec;16(12):1465-79.

Novel patterns of genome rearrangement and their association with survival in breast cancer.

Hicks J, Krasnitz A, Lakshmi B, Navin NE, Riggs M, Leibu E, Esposito D, Alexander J, Troge J, Grubor V, Yoon S, Wigler M, Ye K, Borresen-Dale AL, Naume B, Schlicting E, Norton L, Hagerstrom T, Skoog L, Auer G, Maner S, Lundin P, Zetterberg A.

Examples

data(segexample)