Package 'CNVrd2' reference manual

Title:	CNVrd2: a read depth-based method to detect and genotype complex common copy number variants from next generation sequencing data.
Description:	CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples, indentify SNPs tagging copy number variants and detect copy number polymorphic genomic regions.
Authors:	Hoang Tan Nguyen, Tony R Merriman and Mik Black
Maintainer:	Hoang Tan Nguyen <[email protected]>
License:	GPL-2
Version:	1.43.0
Built:	2024-07-21 05:34:33 UTC
Source:	https://github.com/bioc/CNVrd2

CNVrd2

Description

CNVrd2 uses next-generation sequencing data to measure human gene copy number for multiple samples and indentify SNPs/INDELs which are in linkage disequilibrium with the gene copy number variation.

Details

Package:	CNVrd2
Type:	Package
Version:	1.0
Date:	2013-03-26
License:	GPL-2
Depends:	methods

Author(s)

Maintainer: Hoang Tan Nguyen <[email protected]>

calculateLDSNPandCNV

Description

Identifying SNPs/INDELs being in linkage disequilibrium with CNV.

Usage

calculateLDSNPandCNV(sampleCNV = NULL, vcfFile = NULL,
	matrixGenotype = NULL, cnvColumn = NULL, popColumn = NULL,
	population = NULL, chr = NULL, hg = "hg19", 
	st = NULL, en = NULL,	nChunkForVcf = 10, 
	codeSNP = c("Two", "Three"), codeCNV = c("CN", "ThreeGroup"),
	typeTest = c("All", "Dup", "Del"), parallel = FALSE)

calculateLDSNPandCNV(sampleCNV = NULL, vcfFile = NULL,
	matrixGenotype = NULL, cnvColumn = NULL, popColumn = NULL,
	population = NULL, chr = NULL, hg = "hg19", 
	st = NULL, en = NULL,	nChunkForVcf = 10, 
	codeSNP = c("Two", "Three"), codeCNV = c("CN", "ThreeGroup"),
	typeTest = c("All", "Dup", "Del"), parallel = FALSE)

Arguments

`sampleCNV`	A data frame with no missing data; The first column is samples, the other columns are copy number, population names. This object can be obtained from the clustering step (allGroups).
`vcfFile`	Name of zipped vcf file including SNPs/INDELs.
`matrixGenotype`	A matrix of SNPs/INDELs coded: 0 for 0\|0 or 0/0; 1 for 0/1, 1/0, 0\|1, 1\|0; 2 for 1/1, 1\|1. Rows are SNPs/INDELs and columns are samples. If users use this argument then the argument vcfFile is not necessary.
`cnvColumn`	A number indicating the column of CNV.
`popColumn`	A number indicating the column of a population being calculated LD values.
`population`	A character() vector indicating names of populations.
`chr`	A character string indicating a chromosome of genes/SNPs/INDELs.
`hg`	A character string indicating the version of a reference genome (default: hg19).
`st`	A number indicating a starting coordinate to read the vcf file.
`en`	A number indicating an ending coordinate to read the vcf file.
`nChunkForVcf`	A number indicating how many chunks users would like to divide the vcf file to read into R. It depends on users' computers. We usually use 10 to 50 for this argument.
`codeSNP`	A character string indicating a way to code unphased/phased SNPs/INDELs into numeric values (Two: 0, 1 or Three: 0, 1, 2).
`codeCNV`	A character string partial matching to one of: “All” will test copy-number counts and SNPs/INDELs, “ThreeGroup” will divide samples into three groups: deletion, normality and duplication.
`typeTest`	A character string partial matching to one of: “All” will test all copy-number status, “Dup/Del” will test for only duplicated/deleted versus normal.
`parallel`	A logical value indicating whether multicores are used or NOT (default: FALSE).

Value

r2Andpvalues: A data frame (or a list) with each row for a SNP/INDEL: all information includes p-values adjusted by the method of Benjamini and Hocheberg (1995) and r2 values between the SNP/INDEL and copy-number status.

Note

st and en must not be outside the coordinates of the VCF file.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Benjamini, Y., and Hochberg, Y., 1995. Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological), 57, 289-300.

Examples

##Load data: fcgr3bMXL in CNVrd2 package############
data(fcgr3bMXL)
##Name a vcf file (vcfFile)
vcfFile <- system.file(package="CNVrd2", "extdata",
                      "chr1.161600000.161611000.vcf.gz")
##Make a data fame named sampleCNV including samples, CNs, population names

sampleCNV <- data.frame(copynumberGroups$allGroups[, c(1,2) ],rep("MXL", 58))

rownames(sampleCNV) <- substr(sampleCNV[, 1], 1, 7)
sampleCNV[, 1] <- rownames(sampleCNV)
##The first column must be the sample names 
tagSNPandINDELofMXL <- calculateLDSNPandCNV(sampleCNV = sampleCNV,
                                 vcfFile = vcfFile, cnvColumn = 2,
                                 population = "MXL", popColumn = 3,
                                 nChunkForVcf = 5, chr = "1",
                                 st = 161600000, en = 161611000,
                                 codeSNP= "Three", codeCNV = "ThreeGroup")
tagSNPandINDELofMXL[1:3,]




##Load data: fcgr3bMXL in CNVrd2 package############
data(fcgr3bMXL)
##Name a vcf file (vcfFile)
vcfFile <- system.file(package="CNVrd2", "extdata",
                      "chr1.161600000.161611000.vcf.gz")
##Make a data fame named sampleCNV including samples, CNs, population names

sampleCNV <- data.frame(copynumberGroups$allGroups[, c(1,2) ],rep("MXL", 58))

rownames(sampleCNV) <- substr(sampleCNV[, 1], 1, 7)
sampleCNV[, 1] <- rownames(sampleCNV)
##The first column must be the sample names 
tagSNPandINDELofMXL <- calculateLDSNPandCNV(sampleCNV = sampleCNV,
                                 vcfFile = vcfFile, cnvColumn = 2,
                                 population = "MXL", popColumn = 3,
                                 nChunkForVcf = 5, chr = "1",
                                 st = 161600000, en = 161611000,
                                 codeSNP= "Three", codeCNV = "ThreeGroup")
tagSNPandINDELofMXL[1:3,]

Data of CCL3L1 gene (The 1000 Genomes Project)

Description

This data set includes: segmentation results, population information and CCL3L1 CN.

Usage

data(ccl3l1data)

data(ccl3l1data)

Format

This is a data frame including four columns: Name (names of samples), Pop (names of populations), SS (segmentation scores of samples at the gene) and CN (CCL3L1 CN obtained by using a Bayesian clustering approach with European-ancestry as prior information).

References

www.1000genomes.org

Class `"clusteringCNVs"`

Description

This class is used to cluster segmentation scores into copy-number groups.

Objects from the Class

Objects can be created by calls of the form new("clusteringCNVs", ...).

Slots

x:: Object of class "numeric".
k:: Object of class "numeric" indicating a number of groups.
p:: Object of class "numericOrNULL" indicating groups' proportions.
m:: Object of class "numericOrNULL" indicating groups' means.
sigma:: Object of class "numericOrNULL" indicating groups' standard deviations.
small:: Object of class "numeric" indicating the value to stop the iteration process of the EM algorithm.
nMax:: Object of class "numeric" indicating a maximum number of iterations.
EV:: Object of class "logical" indicating whether all groups having equal variances or not (default).
eee:: Object of class "numeric" indicating a pseudo value of 0.
nmaxInit:: Object of class "numeric" indicating a number of iterations to obtain initial values.
nChangeVariance:: Object of class "numeric" indicating a number of times to change from unequal variances to equal variances (“this option is used to avoid the EM algorithm being broken down if there is one (or a few) sample in a group”).
verbose:: Object of class "logical" indicating whether printing out all loops.
groupDistance:: Object of class "numericOrNULL" indicating the distance between groups.

Methods

emnormalCNV: signature(Object = "clusteringCNVs"): run the EM algorithm.
groupCNVs: signature(Object = "clusteringCNVs"): cluster segmentation scores into groups.
searchGroupCNVs: signature(Object = "clusteringCNVs"): identify a number of groups.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Examples

showClass("clusteringCNVs")
showClass("clusteringCNVs")

Class `"CNVrd2"`

Description

A class of reading BAM files into R and grouping read-count windows into similar segments.

Objects from the Class

Objects can be created by calls of the form new("CNVrd2", ...).

Slots

windows:: Object of class "numeric" indicating a window size.
chr:: Object of class "character" indicating the chromosome of the region.
st:: Object of class "numeric" indicating the starting coordinate of the region.
en:: Object of class "numeric" indicating the ending coordinate of the region.
dirBamFile:: Object of class "character" indicating a directory of BAM files.
dirCoordinate:: Object of class "character" indicating a directory where all the positions of mapped reads will be written out to prepare for the segmentation process.
genes:: Object of class "numeric" indicating gene coordinates.
geneNames:: Object of class "character" indicating names of genes.

Methods

countReadInWindow: signature(Object = "CNVrd2"): Count reads in windows.
plotCNVrd2: signature(Object = "CNVrd2"): Plot traces of samples.
segmentSamples: signature(Object = "CNVrd2"): Cluster windows of read counts into regions having similar signal values.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Examples

showClass("CNVrd2")
showClass("CNVrd2")

Obtain read counts in constant windows.

Description

Counting, transfering and standardizing read counts for all windows of samples. If correctGC = TRUE then all read-count windows will be corrected by the method of Yoon et al. (2009).

Usage

countReadInWindow(Object, ...)
countReadInWindow(Object, ...)

Arguments

`Object`	An object of class CNVrd2.
`...`	Further aguments.

Value

readCountMatrix: a matrix of read counts for all samples (rows).

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Yoon, S., Xuan, Z., Makarov, V., Ye, K., Sebat, J., 2009. Sensitive and accurate detection of copy number variants using read depth of coverage. Genome research 19 (9), 1586-1592.

Examples

## Not run: 
data(fcgr3bMXL)
bamFiles <- dir("Bam", pattern = ".bam$")
objectCNVrd2 <- new("CNVrd2", windows = 1000, chr = "chr1",
                   st = 161100001, en = 162100000,
                   dirBamFile = "Bam",
                   genes = c(161592986, 161601753),
                   geneNames = "3B")

readCountMatrix <- countReadInWindow(Object = objectCNVrd2, correctGC = TRUE)
readCountMatrix[1:3, 1:3]

## End(Not run)
## Not run: 
data(fcgr3bMXL)
bamFiles <- dir("Bam", pattern = ".bam$")
objectCNVrd2 <- new("CNVrd2", windows = 1000, chr = "chr1",
                   st = 161100001, en = 162100000,
                   dirBamFile = "Bam",
                   genes = c(161592986, 161601753),
                   geneNames = "3B")

readCountMatrix <- countReadInWindow(Object = objectCNVrd2, correctGC = TRUE)
readCountMatrix[1:3, 1:3]

## End(Not run)

Method `countReadInWindow`

Description

Method to count reads in windows

Methods

signature(Object = "CNVrd2"): Count, stransfer and standardize read count in windows of samples

Examples

##data(fcgr3bMXL)
##readCountMatrix <- countReadInWindow(Object = objectCNVrd2, correctGC = TRUE)
##readCountMatrix[1:3, 1:3]

##data(fcgr3bMXL)
##readCountMatrix <- countReadInWindow(Object = objectCNVrd2, correctGC = TRUE)
##readCountMatrix[1:3, 1:3]

Implement the EM algorithm

Description

This function is used to obtain the maximization likelihood estimation of normal mixture model by using the EM algorithm (Demster et al., 1977).

Usage

emnormalCNV(Object, ...)
emnormalCNV(Object, ...)

Arguments

`Object`	An object of class clusteringCNVs.
`...`	Optional arguments

Value

`loglk`	Value of the likelihood function.
`p`	Proportions of groups.
`m`	Means of groups.
`sigma`	Standard deviations of groups.
`count`	A number of iteration to obtain convergence stage.
`bic`	See `searchGroupCNVs`.
`z`	Data frame of proportions of data in mixture components.

Note

In the package, the distance between two initial means of the two nearest neighbor groups was set groupDistance = 0.25 as a default value to obtain initial values (using the kmeans function in R).

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1-38.

Examples

data(fcgr3bMXL)

sS <- resultSegment$segmentationScores
#########Histogram###########################
###View segmentation scores##################
hist(sS[, 1], 100)
############################################
##Number of components#######################
###Make an object of clusteringCNVs class######
objectCluster <- new("clusteringCNVs",
                     x = sS[, 1], k = 4, EV = TRUE)

set.seed(123)
copynumberGroups <- groupCNVs(Object = objectCluster)
data(fcgr3bMXL)

sS <- resultSegment$segmentationScores
#########Histogram###########################
###View segmentation scores##################
hist(sS[, 1], 100)
############################################
##Number of components#######################
###Make an object of clusteringCNVs class######
objectCluster <- new("clusteringCNVs",
                     x = sS[, 1], k = 4, EV = TRUE)

set.seed(123)
copynumberGroups <- groupCNVs(Object = objectCluster)

Method `emnormalCNV`

Description

Implement the Expectation Maximization

Methods

signature(Object = "clusteringCNVs")

MXL population data (The 1000 Genomes Project)

Description

This data set includes: segmentation results “resultSegment”, information copy number “copynumberGroups” of FCGR3B gene.

Usage

data(fcgr3bMXL)

data(fcgr3bMXL)

Format

All results analysed by the CNVrd2 package.

References

www.1000genomes.org

groupBayesianCNVs

Description

Cluster segmentation scores into different groups by using prior information from one population.

Usage

groupBayesianCNVs(xData, nGroups, lambda0, sd0, alpha0, distanceBetweenGroups, inits = NULL, 
		precisionOfGroupMeans = 3000, sdOftau = NULL, n.adapt = 100, 
		nUpdate = 1000, n.iter = 20000, thin = 5, n.chains = 1, 
		heidel.diag = FALSE, leftLimit = NULL, rightLimit = NULL)
groupBayesianCNVs(xData, nGroups, lambda0, sd0, alpha0, distanceBetweenGroups, inits = NULL, 
		precisionOfGroupMeans = 3000, sdOftau = NULL, n.adapt = 100, 
		nUpdate = 1000, n.iter = 20000, thin = 5, n.chains = 1, 
		heidel.diag = FALSE, leftLimit = NULL, rightLimit = NULL)

Arguments

`xData`	a numeric vector of observations (segmentation scores).
`nGroups`	an integer indicating a number of groups.
`lambda0`	Prior means of groups.
`sd0`	Prior standard deviations of groups.
`alpha0`	Prior parameters for mixing proportions.
`distanceBetweenGroups`	Prior value for the distance between groups.
`inits`	A list of initial values of parameters.
`precisionOfGroupMeans`	Prior parameter of group means (default = 3000).
`sdOftau`	Prior parameter of the standard deviations of group precisions.
`n.adapt`	the number of iterations for adaptation (`rjags`'s parameter)
`nUpdate`	the number of iterations for burn-in process.
`n.iter`	the number of iterations for sampling (`rjags`'s parameter)
`thin`	thinning interval for monitors (`rjags`'s parameter)
`n.chains`	the number of parallel chains for the model (`rjags`'s parameter, default = 1)
`heidel.diag`	If heidel.diag = TRUE then Heidelberger and Welch's convergence diagnostic is used.
`leftLimit`	Values which are less than this value will be allocated to the smallest group.
`rightLimit`	Values which are larger than this value will be allocated to the largest group.

Details

This function assumes that users already know the information of groups' means, standard deviations; the distances between groups.

Value

`mcmcChains`	A list of mcarray objects for means, standard deviations, proportions
`m1`	Means of groups
`s1`	Standard deviations of groups
`p1`	Proportions of groups
`allGroups`	A data.frame includes samples and their corresponding groups
`hTest`	Results of Heidelberger and Welch's convergence diagnostic

Note

#####

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Martyn Plummer (2013). rjags: Bayesian graphical models using MCMC. R package version 3-10. http://CRAN.R-project.org/package=rjags.

Lunn, David J., et al. WinBUGS-a Bayesian modelling framework: concepts, structure, and extensibility. Statistics and Computing 10.4 (2000): 325-337.

Examples

## Not run: 
data(ccl3l1data)

xyEuro <- ccl3l1data[grep("CEU|TSI|IBS|GBR|FIN", ccl3l1data[, 2]), ]

names(yEuro) <- rownames(xyEuro)

##Clustering European segmentation scores into group: 5 groups were chosen

objectClusterEuroCCL3L1 <- new("clusteringCNVs", x = yEuro, k = 5)

europeanCCL3L1Groups <- groupCNVs(Object = objectClusterEuroCCL3L1)

##Obtain prior information
#Means
lambda0 <- as.numeric(europeanCCL3L1Groups$m)
#SD
sdEM <- as.numeric(europeanCCL3L1Groups$sigma)
#Proportions
pEM <- as.numeric(europeanCCL3L1Groups$p)


###Calculate the distances between groups
for (ii in 2:5){print(lambda0[ii] - lambda0[ii-1])}

###All segmentation scores
ccl3l1X <- ccl3l1data$SS
names(ccl3l1X) <- as.character(ccl3l1data$Name)
range(ccl3l1X)


 
##Set prior information: 
#prior for the sd of the means of groups: 
#5 was set for the third group = 2 CN
sd <- c(1, 1, 5, 1, 1) 
ccl3l1X <- sort(ccl3l1X)
###Data
xData <- ccl3l1X
###Number of groups
nGroups <- 10 
###prior for means of groups
lambda0 <- lambda0 
###Prior for mixing proportions
alpha0 <-  c(3, 29, 44, 18, 7,  5, rep(2, nGroups -length(pEM) -1))
##Prior for the distances between groups
distanceBetweenGroups = 0.485

sdEM = sdEM


##Adjust standard deviation for the fifth group
sdEM[5] <- sdEM[4]
 
 set.seed(123)
 groupCCL3L1allPops <- groupBayesianCNVs(xData = xData, nGroups = nGroups,
                                         lambda0 = lambda0,
                                         sd0 = sdEM, alpha0 = alpha0,
                                         distanceBetweenGroups = distanceBetweenGroups,
                                         sdOftau = sd,
                                        rightLimit = 4)




## End(Not run)
## Not run: 
data(ccl3l1data)

xyEuro <- ccl3l1data[grep("CEU|TSI|IBS|GBR|FIN", ccl3l1data[, 2]), ]

names(yEuro) <- rownames(xyEuro)

##Clustering European segmentation scores into group: 5 groups were chosen

objectClusterEuroCCL3L1 <- new("clusteringCNVs", x = yEuro, k = 5)

europeanCCL3L1Groups <- groupCNVs(Object = objectClusterEuroCCL3L1)

##Obtain prior information
#Means
lambda0 <- as.numeric(europeanCCL3L1Groups$m)
#SD
sdEM <- as.numeric(europeanCCL3L1Groups$sigma)
#Proportions
pEM <- as.numeric(europeanCCL3L1Groups$p)


###Calculate the distances between groups
for (ii in 2:5){print(lambda0[ii] - lambda0[ii-1])}

###All segmentation scores
ccl3l1X <- ccl3l1data$SS
names(ccl3l1X) <- as.character(ccl3l1data$Name)
range(ccl3l1X)


 
##Set prior information: 
#prior for the sd of the means of groups: 
#5 was set for the third group = 2 CN
sd <- c(1, 1, 5, 1, 1) 
ccl3l1X <- sort(ccl3l1X)
###Data
xData <- ccl3l1X
###Number of groups
nGroups <- 10 
###prior for means of groups
lambda0 <- lambda0 
###Prior for mixing proportions
alpha0 <-  c(3, 29, 44, 18, 7,  5, rep(2, nGroups -length(pEM) -1))
##Prior for the distances between groups
distanceBetweenGroups = 0.485

sdEM = sdEM


##Adjust standard deviation for the fifth group
sdEM[5] <- sdEM[4]
 
 set.seed(123)
 groupCCL3L1allPops <- groupBayesianCNVs(xData = xData, nGroups = nGroups,
                                         lambda0 = lambda0,
                                         sd0 = sdEM, alpha0 = alpha0,
                                         distanceBetweenGroups = distanceBetweenGroups,
                                         sdOftau = sd,
                                        rightLimit = 4)




## End(Not run)

Cluster segmentation scores into groups.

Description

Use the EM algorithm (Dempster et al., 1977) to cluster segmentation scores into various groups.

Usage

groupCNVs(Object, ...)
groupCNVs(Object, ...)

Arguments

`Object`	An object of class clusteringCNVs.
`...`	Further arguments.

Details

Users can set limits of segmentation scores: values being smaller than the left limit will be assigned to the smallest group and values being larger than righ limit will be assigned to the largest group.

Value

`allGroups`	Samples and their corresponding groups
`means`	Means of groups.
`sigma`	Variances of groups.
`p`	Proportions of groups in all data set.
`loglk`	Value of loglikehood function.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Dempster, A. P., Laird, N. M., Rubin, D. B., 1977. Maximum likelihood from incomplete data via the em algorithm. Journal of the Royal Statistical Society. Series B (Methodological), 1-38.

Examples

data("fcgr3bMXL")
#resultSegment <- segmentSamples(Object = objectCNVrd2, stdCntMatrix = readCountMatrix)
objectCluster <- new("clusteringCNVs",
                     x = resultSegment$segmentationScores[, 1], k = 4, EV = TRUE)

#searchGroupCNVs(Object = objectCluster)
copynumberGroups <- groupCNVs(Object = objectCluster)

data("fcgr3bMXL")
#resultSegment <- segmentSamples(Object = objectCNVrd2, stdCntMatrix = readCountMatrix)
objectCluster <- new("clusteringCNVs",
                     x = resultSegment$segmentationScores[, 1], k = 4, EV = TRUE)

#searchGroupCNVs(Object = objectCluster)
copynumberGroups <- groupCNVs(Object = objectCluster)

Method `groupCNVs`

Description

Method groupCNVs

Methods

signature(Object = "clusteringCNVs")

Identity polymorphic regions.

Description

Using quantile values to identify polymorphic regions.

Usage

identifyPolymorphicRegion(Object, ...)
identifyPolymorphicRegion(Object, ...)

Arguments

Object

An object of class CNVrd2

...

polymorphicRegionObjectAn object obtained from the process of identifying poplymorphic regions.

Optional arguments.

Value

`putativeBoundary`	resultant boundaries based on quantile thresholds.
`subRegionMatrix`	segmentation-score matrix of sub-regions derived from the segmentation process.
`subRegion`	sub-regions derived from the segmentation process.
`mQuantile`	matrix of quantile values.
`mSD`	data frame including subregions and their standard deviations.
`Vst`	data frame of Vst between populations if VstTest = TRUE.
`SSofPolymorphicRegions`	segmentation scores of polymorphic regions.

Note

Users can choose various quantile values and adjust different thresholds to obtain polymorphic regions.

To visualize more clearly polymorphic regions user can use the method plotPolymorphicRegion with the option typePlot="SD".

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Examples

## Not run: 

fcgr3PolymorphicRegion <- identifyPolymorphicRegion(Object = objectCNVrd2,
                                                    segmentObject = resultSegment, 
                                                    thresholdForPolymorphicRegions = c(0.75, 0.25),
                                                    plotLegend = FALSE)



## End(Not run)
## Not run: 

fcgr3PolymorphicRegion <- identifyPolymorphicRegion(Object = objectCNVrd2,
                                                    segmentObject = resultSegment, 
                                                    thresholdForPolymorphicRegions = c(0.75, 0.25),
                                                    plotLegend = FALSE)



## End(Not run)

Methods for Function `identifyPolymorphicRegion`

Description

Methods for function identifyPolymorphicRegion

Methods

signature(Object = "CNVrd2")

Class `"numericOrNULL"`

Description

Auxiliary classes; may contain either a numeric vector or NULL [or a call / data.frame or NULL, respectively].

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "numericOrNULL" in the signature.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Plot traces of samples.

Description

Plot traces of samples.

Usage

plotCNVrd2(Object, ...)
plotCNVrd2(Object, ...)

Arguments

`Object`	An object of class CNVrd2.
`...`	Optional arguments.

Value

Plot

Note

Users can plot multiple samples simultaneously.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Examples

data(fcgr3bMXL)
##Obtain all information of CNVs
allGroups <- copynumberGroups$allGroups
###Obtain names of duplicate samples
duplicatedSamples <- rownames(allGroups[allGroups[, 2] > 2,])
###Plot the first duplicate samples
par(mfrow = c(3, 2))
for (ii in duplicatedSamples[1:6])  
plotCNVrd2(Object = objectCNVrd2,
           segmentObject = resultSegment,
           sampleName = ii)

data(fcgr3bMXL)
##Obtain all information of CNVs
allGroups <- copynumberGroups$allGroups
###Obtain names of duplicate samples
duplicatedSamples <- rownames(allGroups[allGroups[, 2] > 2,])
###Plot the first duplicate samples
par(mfrow = c(3, 2))
for (ii in duplicatedSamples[1:6])  
plotCNVrd2(Object = objectCNVrd2,
           segmentObject = resultSegment,
           sampleName = ii)

Method `plotCNVrd2`

Description

Method plotCNVrd2

Methods

signature(Object = "CNVrd2")

Plot polymorphic regions.

Description

Plot polymorphic regions based on coordinates set by users.

Usage

plotPolymorphicRegion(Object, ...)
plotPolymorphicRegion(Object, ...)

Arguments

Object

An object of class CNVrd2

...

polymorphicRegionObjectAn object obtained from the process of identifying poplymorphic regions.

Optional arguments.

Value

putativeBoundary

Putative boundaries of polymorphic regions based on quantile values.

Note

Users can choose various quantile values and adjust different thresholds to obtain polymorphic regions.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

Examples

## Not run: 

plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), drawThresholds = TRUE,
                      thresholdForPolymorphicRegions = c(0.75, 0.25))



##Change thresholds
plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), drawThresholds = TRUE,
                      thresholdForPolymorphicRegions = c(0.9, 0.1))


##Plot standard deviation

plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), typePlot = "SD",
                      thresholdForPolymorphicRegions = c(0.75, 0.25))






## End(Not run)
## Not run: 

plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), drawThresholds = TRUE,
                      thresholdForPolymorphicRegions = c(0.75, 0.25))



##Change thresholds
plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), drawThresholds = TRUE,
                      thresholdForPolymorphicRegions = c(0.9, 0.1))


##Plot standard deviation

plotPolymorphicRegion(Object = objectCNVrd2, polymorphicRegionObject = fcgr3PolymorphicRegion,
                      xlim = c(161300000, 161800000), typePlot = "SD",
                      thresholdForPolymorphicRegions = c(0.75, 0.25))






## End(Not run)

Methods for Function `plotPolymorphicRegion`

Description

Methods for function plotPolymorphicRegion

Methods

signature(Object = "CNVrd2")

Choose a number of CN groups

Description

Choose a number of CN groups by using Bayesian Information Criterion (Schwarz, 1978).

Usage

searchGroupCNVs(Object, ...)
searchGroupCNVs(Object, ...)

Arguments

`Object`	An object of class clusteringCNVs.
`...`	Optional arguments.

Value

`Groups`	General information.
`nComponents`	A suitable number of components.

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Schwarz, G. , 1978. Estimating the dimension of a model. The Annals of Statistics 6(2), 461-464.

Method `searchGroupCNVs`

Description

Method searchGroupCNVs

Methods

signature(Object = "clusteringCNVs")

Implement the segmentation process

Description

Segment read-count windows into region having similar signal values by using the DNAcopy package (Venkatraman and Olshen, 2007) and refine this process to obtain segmentation scores at genes.

Usage

segmentSamples(Object, ...)
segmentSamples(Object, ...)

Arguments

`Object`	An object of class CNVrd2.
`...`	Optional arguments.

Value

`segmentResults`	All results of the segmentation process.
`segmentationScores`	Segmentation scores of the gene(s) being measured.
`observedReadCountRatios`	Observed read-count ratios of genes. This value is a matrix of observed read-count ratios at genes if (only inputBamFile = TRUE).
`stdCntMatrix`	Matrix of read counts (standardized).

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Venkatraman, E., Olshen, A. B., 2007. A faster circular binary segmentation algorithm for the analysis of array chg data. Bioinformatics 23 (6), 657-663.

Examples

data(fcgr3bMXL)
## Not run: resultSegment <- segmentSamples(Object = objectCNVrd2, stdCntMatrix = readCountMatrix)
data(fcgr3bMXL)
## Not run: resultSegment <- segmentSamples(Object = objectCNVrd2, stdCntMatrix = readCountMatrix)

Method `segmentSamples`

Description

Method segmentSamples

Methods

signature(Object = "CNVrd2")

Implement the segmentation process for multiple populations

Description

Segment read-count windows into region having similar signal values by using the DNAcopy package (Venkatraman and Olshen, 2007) and refine this process to obtain segmentation scores at genes. Then, the function adjusts segmentation scores for multiple populations using a linear regression model.

Usage

segmentSamplesUsingPopInformation(Object, ...)
segmentSamplesUsingPopInformation(Object, ...)

Arguments

`Object`	An object of class CNVrd2.
`...`	Optional arguments.

Value

`segmentationScores`	All adjusted results of the segmentation process.
`segmentationScoresFromSinglePops`	Segmentation scores of single populations.
`segmentResults`	Un-adjusted segmentation scores
`observedReadCountRatios`	Observed read-count ratios of genes. This value is a matrix of observed read-count ratios at genes if (only inputBamFile = TRUE).
`stdCntMatrix`	Matrix of read counts (standardized).

Author(s)

Hoang Tan Nguyen, Tony R Merriman and MA Black. [email protected]

References

Venkatraman, E., Olshen, A. B., 2007. A faster circular binary segmentation algorithm for the analysis of array chg data. Bioinformatics 23 (6), 657-663.

Method `segmentSamplesUsingPopInformation`

Description

Method segmentSamplesUsingPopInformation

Methods

signature(Object = "CNVrd2")

Class `"vectorORfactor"`

Description

Auxiliary class

Objects from the Class

A virtual Class: No objects may be created from it.

Methods

No methods defined with class "vectorORfactor" in the signature.

Author(s)

Hoang Tan Nguyen, Tony Merriman and Mik Black. [email protected]

Package 'CNVrd2'

Help Index

CNVrd2

Description

Details

Author(s)

calculateLDSNPandCNV

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Data of CCL3L1 gene (The 1000 Genomes Project)

Description

Usage

Format

References

Class "clusteringCNVs"

Description

Objects from the Class

Slots

Methods

Author(s)

Examples

Class "CNVrd2"

Description

Objects from the Class

Slots

Methods

Author(s)

Examples

Obtain read counts in constant windows.

Description

Usage

Arguments

Value

Author(s)

References

Examples

Method countReadInWindow

Description

Methods

See Also

Examples

Implement the EM algorithm

Description

Usage

Arguments

Value

Note

Author(s)

References

See Also

Examples

Method emnormalCNV

Description

Methods

MXL population data (The 1000 Genomes Project)

Description

Usage

Format

References

groupBayesianCNVs

Description

Usage

Arguments

Details

Value

Note

Author(s)

References

See Also

Examples

Cluster segmentation scores into groups.

Description

Usage

Class `"clusteringCNVs"`

Class `"CNVrd2"`

Method `countReadInWindow`

Method `emnormalCNV`

Method `groupCNVs`

Methods for Function `identifyPolymorphicRegion`

Class `"numericOrNULL"`

Method `plotCNVrd2`

Methods for Function `plotPolymorphicRegion`

Method `searchGroupCNVs`

Method `segmentSamples`