Package 'oligoClasses'

Description

Provides a listing of available Affymetrix platforms currently supported by the R package oligo

Usage

affyPlatforms()

Value

A vector of class character.

Author(s)

R. Scharpf

Examples

affyPlatforms()

Description

A class for storing the locus-level summaries of the normalized intensities

Objects from the Class

Objects can be created by calls of the form new("AlleleSet", assayData, phenoData, featureData, experimentData, annotation, protocolData, ...).

Slots

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

featureData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAME" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "eSet", directly. Class "VersionedBiobase", by class "eSet", distance 2. Class "Versioned", by class "eSet", distance 3.

Methods

allele

signature(object = "AlleleSet"): extract allele specific summaries. For 50K (XBA and Hind) and 250K (Sty and Nsp) arrays, an additional argument (strand) must be used (allowed values: 'sense', 'antisense'.

bothStrands

signature(object = "AlleleSet"): tests if data contains allele summaries on both strands for a given SNP.

bothStrands

signature(object = "SnpFeatureSet"): tests if data contains allele summaries on both strands for a given SnpFeatureSet.

db

signature(object = "AlleleSet"): link to database connection.

getA

signature(object = "AlleleSet"): average intensities (across alleles)

getM

signature(object = "AlleleSet"): log-ratio (Allele A vs. Allele B)

Author(s)

R. Scharpf

See Also

SnpSuperSet, CNSet

Examples

showClass("AlleleSet")
## an empty AlleleSet
x <- new("matrix")
new("AlleleSet", senseAlleleA=x, senseAlleleB=x,
    antisenseAlleleA=x, antisenseAlleleB=x)
##or
new("AlleleSet", alleleA=x, alleleB=x)

Description

annotationPackages will return a character vector of the names of annotation packages.

Usage

annotationPackages()

Value

a character vector of the names of annotation packages

Description

Batch statistics used for estimating copy number are stored as AssayData in the 'batchStatistics' slot of the CNSet class. Each element in the AssayData must have the same number of rows and columns. Rows correspond to features and columns correspond to batch.

Objects from the Class

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

Methods

batchNames

signature(object = "AssayData"): ...

batchNames<-

signature(object = "AssayData"): ...

corr

signature(object = "AssayData", allele = "character"): ...

nu

signature(object = "AssayData", allele = "character"): ...

phi

signature(object = "AssayData", allele = "character"): ...

Details

lM: Extracts entire list of linear model parameters.

corr: The within-genotype correlation of log2(A) and log2(B) intensities.

nu: The intercept for the linear model. The linear model is fit to the A and B alleles independently.

phi: The slope for the linear model. The linear model is fit independently to the A and B alleles.

See Also

CNSet-class

Examples

library(crlmm)
library(Biobase)
data(cnSetExample, package="crlmm")
cnSet <- cnSetExample
isCurrent(cnSet)
assayDataElementNames(batchStatistics(cnSet))
## Accessors for linear model parameters
## -- Included here primarily as a check that accessors are working
## -- Values are all NA until CN estimation is performed using the crlmm package
##
## subsetting
cnSet[1:10, ]
## names of elements in the object
## accessors for parameters
nu(cnSet, "A")[1:10, ]
nu(cnSet, "B")[1:10, ]
phi(cnSet, "A")[1:10, ]
phi(cnSet, "B")[1:10, ]

Description

The eSetList-derived classes have an assayDataList slot instead of an assayData slot.

Usage

AssayDataList(storage.mode = c("lockedEnvironment", "environment", "list"), ...)

Arguments

Value

environment

Author(s)

R.Scharpf

See Also

assayDataNew

Examples

r <- replicate(5, matrix(rnorm(25),5,5), simplify=FALSE)
r <- lapply(r, function(x,dns) {dimnames(x) <- dns; return(x)}, dns=list(letters[1:5], LETTERS[1:5]))
ad <- AssayDataList(r=r)
ls(ad)

Description

Accessor for slot assayDataList in Package oligoClasses

Methods

signature(object = "gSetList")

An object inheriting from class gSetList.

signature(object = "oligoSetList")

An object inheriting from class gSetList.

Description

Copy number estimates are susceptible to systematic differences between groups of samples that were processed at different times or by different labs. While 'batch' is often unknown, a useful surrogates is often the scan date of the arrays (e.g., the month of the calendar year) or the 96 well chemistry plate on which the samples were arrayed during lab processing.

Usage

batch(object)
batchNames(object)
batchNames(object) <- value

Arguments

Value

The method 'batch' returns a character vector that has the same length as the number of samples in the CNSet object.

Author(s)

R. Scharpf

See Also

CNSet-class

Examples

a <- matrix(1:25, 5, 5)
colnames(a) <- letters[1:5]
object <- new("CNSet", alleleA=a, batch=rep("batch1", 5))
batch(object)
batchNames(object)

Description

The batchStatistics slot contains statistics estimated from each batch that are used to derive copy number estimates.

Usage

batchStatistics(object)
batchStatistics(object) <- value

Arguments

Details

An object of class AssayData for slot batchStatistics is initialized automatically when creating a new CNSet instance. Required in the call to new is a factor called batch whose unique values determine the number of columns for each assay data element.

Value

batchStatics is an accessor for the slot batchStatistics that returns an object of class AssayData.

See Also

CNSet-class, batchNames, batch

Description

A container for log R ratios and B allele frequencies from SNP arrays.

Objects from the Class

Objects can be created by calls of the form new("BeadStudioSet", assayData, phenoData, featureData, experimentData, annotation, protocolData, baf, lrr, ...).

Slots

featureData:

Object of class "GenomeAnnotatedDataFrame" ~~

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAxE" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

genome:

Object of class "character" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "gSet", directly. Class "eSet", by class "gSet", distance 2. Class "VersionedBiobase", by class "gSet", distance 3. Class "Versioned", by class "gSet", distance 4.

Methods

In the methods below, object has class BeadStudioSet.

baf(object): accessor for the matrix of B allele frequencies.

baf(object) <- value replacement method for B allele frequencies: value must be a matrix of integers.

as(object, "data.frame"): coerce to data.frame with column headers 'lrr', 'baf', 'x' (physical position with unit Mb), 'id', and 'is.snp'. Used for plotting with lattice.

copyNumber(object): accessor for log R ratios.

copyNumber(object) <- value: replacement method for the log R ratios

initialize

signature(.Object = "BeadStudioSet"): constructs an instance of the class

lrr(object): accessor for matrix of log R ratios

lrr(object) <- value replacement method for log R ratios: value should be a matrix or a ff_matrix.

show(object): print a short summary of the BeadStudioSet object.

updateObject(object): update a BeadStudioSet object.

Author(s)

R. Scharpf

Examples

new("BeadStudioSet")

Description

Container for log R ratios and B allele frequencies stored by chromosome.

Slots

assayDataList:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

featureDataList:

Object of class "list" ~~

chromosome:

Object of class "integer" ~~

annotation:

Object of class "character" ~~

genome:

Object of class "character" indicating the genome build. Valid entries are "hg18" and "hg19".

Methods defined for the class

clone2(object, id, prefix="",...)

Performs a deep copy of the ff objects in the assay data elements of object. A new object of the same class will be instantiated. The ff objects in the instantiated object will point to ff files on disk with prefix given by the argument prefix.

A use-case for such a function is that one may want to perform wave correction on the log R ratios in object, but keep a copy of the original unadjusted log R ratios. If object is not copied using clone2 prior to wave correction, the log R ratios will be updated on disk and the original, unadjusted log R ratios will no longer be available.

Accessors

baf(object)An accessor for the B allele frequencies (BAFs). The accessor returns a list where each element of the list is a matrix of the BAFs for the corresponding element in the SetList object. While the BAFs have a range [0, 1], they are often saved internally as integers by multiplying the original BAFs by 1000. Users can restore the original scale by dividing by 1000.

lrr(object) An accessor for the log R ratios, an estimate of the copy number (presumably relative to diploid copy number) at each marker on a SNP array. The accessor returns a list where each element of the list is a matrix of the log R ratios for the corresponding element in the SetList object. The log R ratios are often saved internally as integers by multiplying the original LRRs by 100 in order to reduce the memory footprint of large studies. Users can restore the original scale by dividing by 100.

Author(s)

R. Scharpf

See Also

See supporting packages for methods defined for the class.

Description

Parses cel file dates from the header of .CEL files for the Affymetrix platform

Usage

celfileDate(filename)

Arguments

Value

character string

Author(s)

H. Jaffee

Examples

require(hapmapsnp6)
path <- system.file("celFiles", package="hapmapsnp6")
celfiles <- list.celfiles(path, full.names=TRUE)
dts <- sapply(celfiles, celfileDate)

Description

Returns the complete cel file (including path) for a CNSet object

Usage

celfileName(object)

Arguments

Value

Character string vector.

Note

If the CEL files for an experiment are relocated, the datadir should be updated accordingly. See examples.

Author(s)

R. Scharpf

Examples

## Not run: 
    if(require(crlmm)){
         data(cnSetExample, package="crlmm")
	 celfileName(cnSetExample)
    }
  
## End(Not run)

Description

Only loads an object if the object name is not in the global environment. If not in the global environment and the file exists, the object is loaded (by default). If the file does not exist, the function FUN is run.

Usage

checkExists(.name, .path = ".", .FUN, .FUN2, .save.it=TRUE, .load.it, ...)

Arguments

Value

Could be anything – depends on what FUN, FUN2 perform.

Future versions could return a 0 or 1 indicating whether the function performed as expected.

Author(s)

R. Scharpf

Examples

path <- tempdir()
dir.create(path)
x <- 3+6
x <- checkExists("x", .path=path, .FUN=function(y, z) y+z, y=3, z=6)
rm(x)
x <- checkExists("x", .path=path, .FUN=function(y, z) y+z, y=3, z=6)
rm(x)
x <- checkExists("x", .path=path, .FUN=function(y, z) y+z, y=3, z=6)
rm(x)
##now there is a file called x.rda in tempdir().  The file will be loaded
x <- checkExists("x", .path=path, .FUN=function(y, z) y+z, y=3, z=6)
rm(x)
unlink(path, recursive=TRUE)

Description

Checks whether a eSet-derived class (e.g., a SnpSet or CNSet object) is ordered by chromosome and physical position

Usage

checkOrder(object, verbose = FALSE)
chromosomePositionOrder(object, ...)

Arguments

Details

Checks whether the object is ordered by chromosome and physical position.

Value

Logical

Author(s)

R. Scharpf

See Also

order

Examples

data(oligoSetExample)
if(!checkOrder(oligoSet)){
   oligoSet <- chromosomePositionOrder(oligoSet)
}
checkOrder(oligoSet)

Description

Methods for function chromosome in package oligoClasses ~~

Methods

The methods for chromosome extracts the chromosome (represented as an integer) for each marker in a eSet-derived class or a AnnotatedDataFrame-derived class.

signature(object = "AnnotatedDataFrame")

Accessor for chromosome.

signature(object = "eSet")

If 'chromosome' is included in fvarLabels(object), the integer representation of the chromosome will be returned. Otherwise, an error is thrown.

signature(object = "GenomeAnnotatedDataFrame")

Accessor for chromosome. If annotation was not available due to a missing or non-existent annotation package, the value returned by the accessor will be a vector of zero's.

(chromosome(object) <- value): Assign chromosome to the AnnotatedDataFrame slot of an eSet-derived object.

signature(object = "RangedDataCNV")

Accessor for chromosome.

Note

Integer representation: chr X = 23, chr Y = 24, chr XY = 25. Symbols M, Mt, and MT are coded as 26.

See Also

chromosome2integer

Examples

chromosome2integer(c(1:22, "X", "Y", "XY", "M"))

Description

Coerces character string for chromosome in the pd. annotation packages to integers

Usage

chromosome2integer(chrom)
        integer2chromosome(intChrom)

Arguments

Details

This is useful when sorting SNPs in an object by chromosome and physical position – ensures that the sorting is done in the same way for different objects.

Value

integer2chromosome returns a vector of character string indicating the chromosome the same length as intChrom. chromosome2integer returns a vector of integers the same length as the number of elements in the chrom vector.

Author(s)

R. Scharpf

Examples

chromosome2integer(c(1:22, "X", "Y", "XY", "M"))
	integer2chromosome(chromosome2integer(c(1:22, "X", "Y", "XY", "M")))

Description

CNSet is a container for intermediate data and parameters pertaining to allele-specific copy number estimation. Methods for CNSet objects, including accessors for linear model parameters and allele-specific copy number are included here.

Objects from the Class

An object from the class is not generally intended to be initialized by the user, but returned by the genotype function in the crlmm package.

The following creates a very basic CNSet with assayData containing the required elements.

new(CNSet, alleleA=new("matrix"), alleleB=new("matrix"), call=new("matrix"), callProbability=new("matrix"), batch=new("factor"))

Slots

batch:

Object of class "factor" ~~

batchStatistics:

Object of class "AssayData" ~~

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

featureData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAME" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

datadir:

Object of class "list"~~

mixtureParams:

Object of class "matrix"~~

.__classVersion__:

Object of class "Versions" ~~

Methods

The argument object for the following methods is a CNSet.

object[i, j]: subset the CNSet object by markers (i) and/or samples (j).

A(objet): accessor for the normalized intensities of allele A

A(object) <- value: replace intensities for the A allele intensities by value. The object value must be a matrix, ff_matrix, or ffdf.

allele(object, allele): accessor for the normalized intensities for the A or B allele. The argument for allele must be either 'A' or 'B'

B(objet): accessor for the normalized intensities of allele B

B(object) <- value: replace intensities for the B allele intensities by value. The object value must be a matrix, ff_matrix, or ffdf.

batch(object): vector of batch labels for each sample.

batchNames(object): the unique batch names

batchNames(object) <- value: relabel the batches

calls(object): accessor for genotype calls coded as 1 (AA), 2 (AB), or 3 (BB). Nonpolymorphic markers are NA.

confs(object): accessor for the genotype confidence scores.

close(object): close any open file connections to ff objects stored in the CNSet object.

as(object, "oligoSnpSet"): coerce a CNSet object to an object of class oligoSnpSet – a container for the total copy number and genotype calls.

corr(object): the correlation of the A and B intensities within each genotype.

flags(object): flags to indicate possible problems with the copy number estimation. Not fully implemented at this point.

new("CNSet"): instantiating a CNSet object.

nu(object, allele): accessor for the intercept (background) for the A and B alleles. The value of allele must be 'A' or 'B'.

open(object) open file connections for all ff objects stored in the CNSet object.

nu(object, allele): accessor for the slope for the A and B alleles. The value of allele must be 'A' or 'B'.

sigma2(object, allele): accessor for the within genotype variance

tau2(object, allele): accessor for background variance

Author(s)

R. Scharpf

Examples

new("CNSet")

Description

Container for storing total copy number estimates and confidence scores of the copy number estimates.

Objects from the Class

Objects can be created by calls of the form new("CopyNumberSet", assayData, phenoData, featureData, experimentData, annotation, protocolData, copyNumber, cnConfidence, ...).

Slots

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

featureData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAxE" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "eSet", directly. Class "VersionedBiobase", by class "eSet", distance 2. Class "Versioned", by class "eSet", distance 3.

Methods

cnConfidence

signature(object = "CopyNumberSet"): ...

cnConfidence<-

signature(object = "CopyNumberSet", value = "matrix"): ...

coerce

signature(from = "CNSet", to = "CopyNumberSet"): ...

copyNumber

signature(object = "CopyNumberSet"): ...

copyNumber<-

signature(object = "CopyNumberSet", value = "matrix"): ...

initialize

signature(.Object = "CopyNumberSet"): ...

Note

This container is primarily for platforms for which genotypes are unavailable. As oligoSnpSet extends this class, methods related to total copy number that do not depend on genotypes can be defined at this level.

Author(s)

R. Scharpf

See Also

For genotyping platforms, total copy number estimates and genotype calls can be stored in the oligoSnpSet class.

Examples

showClass("CopyNumberSet")
cnset <- new("CopyNumberSet")
ls(Biobase::assayData(cnset))

Description

Accessors and CopyNumberSet

Usage

copyNumber(object, ...)
cnConfidence(object)
copyNumber(object) <- value
cnConfidence(object) <- value

Arguments

Value

copyNumber returns a matrix of copy number estimates or relative copy number estimates. Since the copy number estimates are stored as integers (copy number * 100), the matrix returned by the copyNumber accessor will need to be divided by a factor of 100 to transform the measurements back to the original copy number scale.

cnConfidence returns a matrix of confidence scores for the copy number estimates. These are also represented as integers and will require a back-transformation to the original scale.

Examples

library(Biobase)
	data(locusLevelData)
	path <- system.file("extdata", package="oligoClasses")
	fd <- readRDS(file.path(path, "genomeAnnotatedDataFrameExample.rds"))
	## the following command creates an 'oligoSnpSet' object, storing
	## an integer representation of the log2 copy number in the 'copyNumber' element
	## of the assayData.  Genotype calls and genotype confidence scores are also stored
	## in the assayData.
	oligoSet <- new("oligoSnpSet",
			copyNumber=integerMatrix(log2(locusLevelData[["copynumber"]]/100), 100),
			call=locusLevelData[["genotypes"]],
			callProbability=integerMatrix(locusLevelData[["crlmmConfidence"]], 1),
			annotation=locusLevelData[["platform"]],
			featureData=fd,
			genome="hg19")

	## There are several accessors for the oligoSnpSet class.
	icn <- copyNumber(oligoSet)
	range(icn) ## integer scale
	lcn <- icn/100
	range(lcn) ## log2 copy number

	## confidence scores for the genotypes are also represented on an integer scale
	ipr <- snpCallProbability(oligoSet)
	range(ipr) ## integer scale

	## for genotype confidence scores, the helper function i2p
	## converts back to a probability scale
	pr <- i2p(ipr)
	range(pr)

	## The helper function confs is a shortcut, extracting the
	## integer-based confidence scores and transforming to the
	## probability scale
	pr2 <- confs(oligoSet)
	all.equal(pr, pr2)

	## To extract information on the annotation of the SNPs, one can use
	position(oligoSet)
	chromosome(oligoSet)
	## the position and chromosome coordinates were extracted from build hg19
	genomeBuild(oligoSet)

Description

Creates ff objects (array-like) using settings (path) defined by oligoClasses.

Usage

createFF(name, dim, vmode = "double", initdata = NULL)

Arguments

Value

ff object.

Note

This function is meant to be used by developers.

See Also

ff

Description

This function will return the SQLite connection to the database associated to objects used in oligo.

Usage

db(object)

Arguments

Value

SQLite connection.

Methods

object = "FeatureSet"

object of class FeatureSet

object = "SnpCallSet"

object of class SnpCallSet

object = "DBPDInfo"

object of class DBPDInfo

object = "SnpLevelSet"

object of class SnpLevelSet

Author(s)

Benilton Carvalho

Examples

## db(object)

Description

A class for Platform Design Information objects, stored using a database approach

Objects from the Class

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

Slots

getdb:

Object of class "function"

tableInfo:

Object of class "data.frame"

manufacturer:

Object of class "character"

genomebuild:

Object of class "character"

geometry:

Object of class "integer" with length 2 (rows x columns)

Methods

annotation

string describing annotation package associated to object

Description

Example of ExpressionFeatureSet Object.

Usage

data(efsExample)

Format

Object belongs to ExpressionFeatureSet class.

Examples

data(efsExample)
class(efsExample)

Description

Accessor for the 'exprs'/'se.exprs' slot of FeatureSet-like objects

Methods

object = "ExpressionSet"

Expression matrix for objects of this class. Usually results of preprocessing algorithms, like RMA.

object = "FeatureSet"

General container 'exprs' inherited from eSet

object = "SnpSet"

General container 'exprs' inherited from eSet, not yet used.

Description

Accessor for slot featureDataList in Package oligoClasses ~~

Methods

signature(object = "gSetList")

An object inheriting from class gSetList.

Description

Classes to store data from Expression/Exon/SNP/Tiling arrays at the feature level.

Objects from the Class

The FeatureSet class is VIRTUAL. Therefore users are not able to create instances of such class.

Objects for FeatureSet-like classes can be created by calls of the form: new(CLASSNAME, assayData, manufacturer, platform, exprs, phenoData, featureData, experimentData, annotation, ...). But the preferred way is using parsers like read.celfiles and read.xysfiles.

Slots

manufacturer:

Object of class "character"

assayData:

Object of class "AssayData"

phenoData:

Object of class "AnnotatedDataFrame"

featureData:

Object of class "AnnotatedDataFrame"

experimentData:

Object of class "MIAME"

annotation:

Object of class "character"

.__classVersion__:

Object of class "Versions"

Methods

show

signature(.Object = "FeatureSet"): show object contents

bothStrands

signature(.Object = "SnpFeatureSet"): checks if object contains data for both strands simultaneously (50K/250K Affymetrix SNP chips - in this case it returns TRUE); if object contains data for one strand at a time (SNP 5.0 and SNP 6.0 - in this case it returns FALSE)

Author(s)

Benilton Carvalho

See Also

eSet, VersionedBiobase, Versioned

Examples

set.seed(1)
tmp <- 2^matrix(rnorm(100), ncol=4)
rownames(tmp) <- 1:25
colnames(tmp) <- paste("sample", 1:4, sep="")
efs <- new("ExpressionFeatureSet", exprs=tmp)

Description

~~ A concise (1-5 lines) description of what the class is. ~~

Objects from the Class

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

Slots

.S3Class:

Object of class "character" ~~

Extends

Class "oldClass", directly.

Methods

annotatedDataFrameFrom

signature(object = "ff_matrix"): ...

Examples

showClass("ff_matrix")

Description

A class union of 'ffdf', 'ff_matrix', and 'matrix'

Objects from the Class

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

Methods

GenomeAnnotatedDataFrameFrom

signature(object = "ff_or_matrix"): ...

Author(s)

R. Scharpf

See Also

ff, ffdf

Examples

showClass("ff_or_matrix")

Description

Extended package ff's class definitions for ff to S4.

Objects from the Class

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

Slots

.S3Class:

Object of class ffdf ~~

Extends

Class "oldClass", directly. Class "list_or_ffdf", directly.

Methods

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

Description

CNSet objects can contain ff-derived objects that contain pointers to files on disk, or ordinary matrices. Here we define open and close methods for ordinary matrices and vectors that that simply pass back the original matrix/vector.

Usage

open(con, ...)
openff(object)
closeff(object)

Arguments

Value

not applicable

Author(s)

R. Scharpf

Examples

open(rnorm(15))
open(matrix(rnorm(15), 5,3))

Description

Used to flag SNPs with low minor allele frequencies, or for possible problems during the CN estimation step. Currently, this is primarily more for internal use.

Usage

flags(object)

Arguments

Value

A matrix or ff_matrix object with rows corresponding to markers and columns corresponding to batch.

See Also

batchStatistics

Examples

x <- matrix(runif(250*96*2, 0, 2), 250, 96*2)
test1 <- new("CNSet", alleleA=x, alleleB=x, call=x, callProbability=x,
	      batch=as.character(rep(letters[1:2], each=96)))
dim(flags(test1))

Description

Miscellaneous generics. Methods defined in packages that depend on oligoClasses

Usage

baf(object)
lrr(object)

Arguments

Author(s)

R. Scharpf

Description

AnnotatedDataFrame with genomic coordinates (chromosome, position)

Slots

varMetadata:

Object of class "data.frame" ~~

data:

Object of class "data.frame" ~~

dimLabels:

Object of class "character" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "AnnotatedDataFrame", directly. Class "Versioned", by class "AnnotatedDataFrame", distance 2.

Coercion to or from other classes

as(from, "GenomeAnnotatedDataFrame"):

Coerce an object of class AnnotatedDataFrame to a GenomeAnnotatedDataFrame.

makeFeatureGRanges(object, genome, ...):

Construct a GRanges instance from a GenomeAnnotatedDataFrame object. genome is a character string indicating the UCSC build. Supported builds are "hg18" and "hg19", but are platform specific. In particular, some platforms only support build hg19 at this time.

updateObject(object):

For updating a GenomeAnnotatedDataFrame

Accessors

chromosome(object), chromosome(object) <- value

Get or set chromosome.

isSnp(object):

Many platforms include polymorphic and nonpolymorphic markers. isSnp evalutes to TRUE if the marker is polymorphic.

position(ojbect):

Physical position in the genome

getArm(object, genome):

Retrieve character vector indicating the chromosome arm of each marker in object. genome should indicate which genome build was used to define the chromosomal locations (currently, only UCSC genome builds 'hg18' and 'hg19' supported for this function).

Author(s)

R. Scharpf

Description

GenomeAnnotatedDataFrameFrom is a convenience for creating GenomeAnnotatedDataFrame objects.

Methods

Use the method with GenomeAnnotatedDataFrameFrom(object, annotationPkg, genome, ...); the argument annotationPkg must be specified for matrix and AssayData classes.

signature(object="assayData")

This method creates an GenomeAnnotatedDataFrame using feature names and dimensions of an AssayData object as a template.

signature(object="matrix")

This method creates an GenomeAnnotatedDataFrame using row names and dimensions of a matrix object as a template.

signature(object="NULL")

This method (called with 'NULL' as the object) creates an empty GenomeAnnotatedDataFrame.

signature(object="array")

This method (called with 'array' as the object) creates a GenomeAnnotatedDataFrame using the first dimension of the array (rows are the number of features).

Author(s)

R Scharpf

Examples

require(Biobase)
	minReqVersion <- "1.0.2"
	require(human370v1cCrlmm)
	if (packageDescription("human370v1cCrlmm", fields='Version') >= minReqVersion){
		x <- matrix(1:25, 5, 5,
			    dimnames=list(c("rs10000092","rs1000055", "rs100016", "rs10003241", "rs10004197"), NULL))
		gd <- GenomeAnnotatedDataFrameFrom(x, annotationPkg="human370v1cCrlmm",
						   genome="hg18")
		pData(gd)
		chromosome(gd)
		position(gd)
	}

Description

Returns the genome build. This information comes from the annotation package and is given as an argument during the package creation process.

Usage

genomeBuild(object)

Arguments

Value

character string

Note

Supported builds are UCSC genome builds are 'hg18' and 'hg19'.

Examples

showMethods("genomeBuild", where="package:oligoClasses")

Description

For a given array, geometry returns the physical geometry of it.

Usage

geometry(object)

Arguments

Examples

if (require(pd.mapping50k.xba240))
 geometry(pd.mapping50k.xba240)

Description

Methods to compute average log-intensities and log-ratios across alleles, within strand.

Usage

getA(object)
getM(object)
A(object, ...)
B(object, ...)

Arguments

Details

For SNP data, SNPRMA summarizes the SNP information into 4 quantities (log2-scale):

• antisenseThetaAantisense allele A. (Not applicable for Affymetrix 5.0 and 6.0 platforms.)

• antisenseThetaBantisense allele B. (Not applicable for Affymetrix 5.0 and 6.0 platforms.)

• senseThetaAsense allele A. (Not applicable for Affymetrix 5.0 and 6.0 platforms.)

• senseThataBsense allele B. (Not applicable for Affymetrix 5.0 and 6.0 platforms.)

• alleleAAffymetrix 5.0 and 6.0 platforms

• alleleBAffymetrix 5.0 and 6.0 platforms

The average log-intensities are given by: (antisenseThetaA+antisenseThetaB)/2 and (senseThetaA+senseThetaB)/2.

The average log-ratios are given by: antisenseThetaA-antisenseThetaB and senseThetaA-senseThetaB.

For Tiling data, getM and getA return the log-ratio and average log-intensities computed across channels: M = log2(channel1)-log2(channel2) A = (log2(channel1)+log2(channel2))/2

When large data support is enabled with the ff package, the AssayData elements of an AlleleSet object can be ff_matrix or ffdf, in which case pointers to the ff object are stored in the assay data. The functions open and close can be used to open or close the connection, respectively.

Value

A 3-dimensional array (SNP's x Samples x Strand) with the requested measure, when the input SNP data (50K, 250K).

A 2-dimensional array (SNP's x Samples), when the input is from SNP 5.0 and SNP 6.0 arrays.

A 2-dimensional array if the input is from Tiling arrays.

See Also

snprma

Description

Gets a bar of a given length.

Usage

getBar(width = getOption("width"))

Arguments

Value

character string.

Author(s)

Benilton S Carvalho

Examples

message(getBar())

Description

Load chromosome sequence lengths for UCSC genome build hg18 or hg19

Usage

getSequenceLengths(build)

Arguments

Details

The chromosome sequence lengths for UCSC builds hg18 and hg19 were extracted from the packages BSgenome.Hsapiens.UCSC.hg18 and BSgenome.Hsapiens.UCSC.hg19, respectively.

Value

Names integer vector of chromosome lengths.

Author(s)

R. Scharpf

Examples

getSequenceLengths("hg18")
getSequenceLengths("hg19")

if(require("GenomicRanges")){
## from GenomicRanges
	sl <- getSequenceLengths("hg18")[c("chr1", "chr2", "chr3")]
	gr <-
		GRanges(seqnames =
			Rle(c("chr1", "chr2", "chr1", "chr3"), c(1, 3, 2, 4)),
			ranges =
			IRanges(1:10, width = 10:1, names = head(letters,10)),
			strand =
			Rle(strand(c("-", "+", "*", "+", "-")),
			    c(1, 2, 2, 3, 2)),
			score = 1:10,
			GC = seq(1, 0, length=10),
			seqlengths=sl)
	metadata(gr) <- list(genome="hg18")
	gr
	metadata(gr)
}

Description

Methods for GRanges objects

findOverlaps methods

findOverlaps(query, subject, ...):

Find the feature indices in subject that overlap the genomic intervals in query, where query is a GRanges object and subject is a gSet-derived object. Additional arguments to the findOverlaps method in the package IRanges can be passed through the ... operator.

Accessors

object is an instance of the GRanges class.

coverage2(object):

For the GRanges and GRangesList objects returned by the hidden Markov model implemented in the "VanillaICE" package and the segmentation algorithm in the "MinimumDistance" package, the intervals are annotated by the number of probes (markers) for SNPs and nonpolymorphic regions. coverage2 and numberProbes are convenient accessors for these annotations.

genomeBuild(object):

Accessor for the UCSC genome build.

numberProbes(object):

Integer vector indicating the number of probes (markers) for each range in object. Equivalent to coverage2.

state(object):

Accessor for the elementMetadata column 'state', when applicable. State is used to contain the index of the inferred copy number state for various hmm methods defined in the VanillaICE.

See Also

GRanges

Examples

library(IRanges)
	  library(GenomicRanges)
	  gr1 <- GRanges(seqnames = "chr2", ranges = IRanges(3, 6),
			 state=3L, numberProbes=100L)
	  ## convenience functions
	  state(gr1)
	  numberProbes(gr1)

	  gr2 <- GRanges(seqnames = c("chr1", "chr1"),
			 ranges = IRanges(c(7,13), width = 3),
			 state=c(2L, 2L), numberProbes=c(200L, 250L))
	  gr3 <- GRanges(seqnames = c("chr1", "chr2"),
			 ranges = IRanges(c(1, 4), c(3, 9)),
			 state=c(1L, 4L), numberProbes=c(300L, 350L))
	  ## Ranges organized by sample
	  grl <- GRangesList("sample1" = gr1, "sample2" = gr2, "sample3" = gr3)
	  sampleNames(grl) ## same as names(grl)
	  numberProbes(grl)
	  chromosome(grl)
	  state(grl)
	  gr <- stack(grl)
	  sampleNames(gr)
	  chromosome(gr)
	  state(gr)

Description

Container for objects with genomic annotation on SNPs

Objects from the Class

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

Slots

featureData:

Object of class "GenomeAnnotatedDataFrame" ~~

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAxE" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

genome:

Object of class "character" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "eSet", directly. Class "VersionedBiobase", by class "eSet", distance 2. Class "Versioned", by class "eSet", distance 3.

Methods

The object for the below methods is a class that extends the virtual class gSet.

checkOrder(object): checks that the object is ordered by chromosome and physical position. Returns logical.

chromosome(object): accessor for chromosome in the GenomeAnnotatedDataFrame slot.

chromosome(object) <- value: replacement method for chromosome in the GenomeAnnotatedDataFrame slot. value must be an integer vector.

db(object): database connection

genomeBuild(object), genomeBuild(object) <- value:

Get or set the UCSC genome build. Supported builds are hg18 and hg19.

getArm(object): Character vector indicating the chromosomal arm for each marker in object.

isSnp(object): whether the marker is polymorphic. Returns a logical vector.

makeFeatureGRanges(object): Construct an instance of the GRanges class from a GenomeAnnotatedDataFrame.

position(object): integer vector of the genomic position

show(object):

Print a concise summary of object.

Author(s)

R. Scharpf

See Also

chromosome, position, isSnp

Examples

showClass("gSet")

Description

Virtual Class for Lists of eSets.

Objects from the Class

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

Slots

assayDataList:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAME" ~~

featureDataList:

Object of class "list" ~~

chromosome:

Object of class "vector" ~~

annotation:

Object of class "character" ~~

genome:

Object of class "character" ~~

Accessors

object is an instance of a gSetList-derived class.

annotation(object):

character string indicating the package used to provide annotation for the features on the array.

chromosome(object):

Returns the chromosome corresponding to each element in the gSetList object

elementNROWS(object): Returns the number of rows for each list of assays. In most gSetList-derived classes, the assays are organized by chromosome and elementNROWS returns the number of markers for each chromosome.

genomeBuild(object), genomeBuild(object) <- value:

Get or set the UCSC genome build. Supported builds are hg18 and hg19.

Coercion

object is an instance of a gSetList-derived class.

makeFeatureGRanges(object, ...):

Create a GRanges object for the featureData. The featureData is stored as a list. This method stacks the featureData from each list element. Metadata columns in the GRanges object include physical position ('position'), a SNP indicator ('isSnp'), and the chromosome. The genome build is extracted from object using the method genomeBuild.

Author(s)

R. Scharpf

See Also

oligoSetList, BeadStudioSetList

Examples

showClass("gSetList")

Description

Probabilities estimated in the crlmm package are often stored as integers to save memory. We provide a few utility functions to go back and forth between the probability and integer representations.

Usage

i2p(i)
p2i(p)

Arguments

Value

The value returned by i2p is

1 - exp(-i/1000)

The value returned by 2pi is

as.integer(-1000*log(1-p))

See Also

confs

Examples

i2p(693)
	p2i(0.5)
	i2p(p2i(0.5))

Description

Initialize big matrices or vectors appropriately (conditioned on the status of support for large datasets - see Details).

Usage

initializeBigMatrix(name=basename(tempfile()), nr=0L, nc=0L, vmode = "integer", initdata = NA)
initializeBigVector(name=basename(tempfile()), n=0L, vmode = "integer",
  initdata = NA)
initializeBigArray(name=basename(tempfile()), dim=c(0L,0L,0L),
  vmode="integer", initdata=NA)

Arguments

Details

These functions are meant to be used by developers. They provide means to appropriately create big vectors or matrices for packages like oligo and crlmm (and friends). These objects are created conditioned on the status of support for large datasets.

Value

If the 'ff' package is loaded (in the search path), then an 'ff' object is returned. A regular R vector or array is returned otherwise.

Examples

x <- initializeBigVector("test", 10)
class(x)
x
if (isPackageLoaded("ff"))
  finalizer(x) <- "delete"
rm(x)
initializeBigMatrix(nr=5L, nc=5L)
initializeBigArray(dim=c(10, 5, 3))

Description

Coerce numeric matrix to matrix of integers, retaining dimnames.

Usage

integerMatrix(x, scale = 100)
integerArray(x, scale=100)

Arguments

Value

A matrix or array of integers.

Author(s)

R. Scharpf

Examples

x <- matrix(rnorm(10), 5, 2)
rownames(x) = letters[1:5]
i <- integerMatrix(x, scale=100)

Description

Check if object is an ff-matrix object.

Usage

is.ffmatrix(object)

Arguments

Value

Logical.

Note

This function is meant to be used by developers.

Examples

if (isPackageLoaded("ff")){
  x1 <- ff(vmode="double", dim=c(10, 2))
  is.ffmatrix(x1)
}
x1 <- matrix(0, nr=10, nc=2)
is.ffmatrix(x1)

Description

Checks if package is loaded.

Usage

isPackageLoaded(pkg)

Arguments

Details

Checks if package name is in the search path.

Value

Logical.

See Also

search

Examples

isPackageLoaded("oligoClasses")
isPackageLoaded("ff")
isPackageLoaded("snow")

Description

~~ Methods for function isSnp in package oligoClasses ~~

Methods

Return an indicator for whether the marker is polymorphic (value 1) or nonpolymorphic (value 0).

signature(object = "character", pkgname = "character")

Return an indicator for whether the vector of marker identifiers in object is polymorphic. pkgname must be one of the supported annotation packages specific to the platform.

signature(object = "eSet", pkgname = "ANY")

If 'isSnp' is included in fvarLabels(object), an indicator for polymorphic markers is returned. Otherwise, an error is thrown.

signature(object = "GenomeAnnotatedDataFrame", pkgname = "ANY")

Accessor for indicator of whether the marker is polymorphic. If annotation was not available due to a missing or non-existent annotation package, the value returned by the accessor will be a vector of zero's.

Description

Retrieves the array type.

Usage

kind(object)

Arguments

Value

String: "Expression", "Exon", "SNP" or "Tiling"

Examples

if (require(pd.mapping50k.xba240)){
 data(sfsExample)
 Biobase::annotation(sfsExample) <- "pd.mapping50k.xba240"
 kind(sfsExample)
}

Description

Set/check large dataset options.

Usage

ldSetOptions(nsamples=100, nprobesets=20000, path=getwd(), verbose=FALSE)
ldStatus(verbose=FALSE)
ldPath(path)

Arguments

Details

Some functions in oligo/crlmm can process data in batches to minimize memory footprint. When using this feature, the 'ff' package resources are used (and possibly combined with cluster resources set in options() via 'snow' package).

Methods that are executed on a sample-by-sample manner can use ocSamples() to automatically define how many samples are processed at once (on a compute node). Similarly, methods applied to probesets can use ocProbesets(). Users should set these options appropriately.

ldStatus checks the support for large datasets.

ldPath checks where ff files are stored.

Author(s)

Benilton S Carvalho

See Also

ocSamples, ocProbesets

Examples

ldStatus(TRUE)

Description

Number of samples for FeatureSet-like objects.

Methods

x = "FeatureSet"

Number of samples

Description

Supress package startup messages when loading a library

Usage

library2(...)

Arguments

Author(s)

R. Scharpf

See Also

library

Examples

library2("Biobase")

Description

Function used to get a list of CEL files.

Usage

list.celfiles(..., listGzipped=FALSE)

Arguments

Value

Character vector with filenames.

Note

Quite often users want to use this function to pass filenames to other methods. In this situations, it is safer to use the argument 'full.names=TRUE'.

See Also

list.files

Examples

if (require(hapmapsnp5)){
  path <- system.file("celFiles", package="hapmapsnp5")

  ## only the filenames
  list.celfiles(path)

  ## the filenames with full path...
  ## very useful when genotyping samples not in the working directory
  list.celfiles(path, full.names=TRUE)
}else{
  ## this won't return anything
  ## if in the working directory there isn't any CEL
  list.celfiles(getwd())
}

Description

Initialization method for eSetList virtual class.

Description

This object is a list containing the basic data elements required for the HMM

Usage

data(locusLevelData)

Format

A list

Details

The basic assay data elements that can be used for fitting the HMM are:

1. a mapping of platform identifiers to chromosome and physical position

2. (optional) a matrix of copy number estimates

3. (optional) a matrix of confidence scores for the copy number estimates (e.g., inverse standard deviations)

4. (optional) a matrix of genotype calls

5. (optional) CRLMM confidence scores for the genotype calls

At least (2) or (4) is required. The locusLevelData is a list that contains (1), (2), (4), and (5).

Source

A HapMap sample on the Affymetrix 50k platform. Chromosomal alterations were simulated. The last 100 SNPs on chromosome 2 are, in fact, a repeat of the first 100 SNPs on chromosome 1 – this was added for internal use.

Examples

data(locusLevelData)
str(locusLevelData)

Description

Construct a GRanges object from several possible feature-level classes. The conversion is useful for subsequent ranged-data queries, such as findOverlaps, countOverlaps, etc.

Usage

makeFeatureGRanges(object, ...)

Arguments

Value

A GRanges object.

Author(s)

R. Scharpf

See Also

findOverlaps, GRanges, GenomeAnnotatedDataFrame

Examples

library(oligoClasses)
	library(GenomicRanges)
	library(Biobase)
	library(foreach)
	registerDoSEQ()
	data(oligoSetExample, package="oligoClasses")
	oligoSet <- oligoSet[chromosome(oligoSet) == 1, ]
  makeFeatureGRanges(oligoSet)

Description

Manufacturer ID for FeatureSet-like and DBPDInfo-like objects.

Methods

object = "FeatureSet"

Manufacturer ID

object = "PDInfo"

Manufacturer ID

Description

ocLapply is an lapply-like function that checks if ff/snow are loaded and if the cluster variable is set to execute FUN on a cluster. If these requirements are not available, then lapply is used.

Usage

ocLapply(X, FUN, ..., neededPkgs)

Arguments

Details

neededPkgs is needed when parallel computing is expected to be used. These packages are loaded on the compute nodes before the execution of FUN.

Value

A list of length length(X).

Author(s)

Benilton S Carvalho

See Also

lapply, parStatus

Description

Tools to simplify management of clusters via 'snow' package and large dataset handling through the 'bigmemory' package.

Usage

ocSamples(n)
ocProbesets(n)

Arguments

Details

Some methods in the oligo/crlmm packages, like backgroundCorrect, normalize, summarize and rma can use a cluster (set through the 'foreach' package). The use of cluster features is conditioned on the availability of the 'ff' (used to provide shared objects across compute nodes) and 'foreach' packages.

To use a cluster, 'oligo/crlmm' checks for three requirements: 1) 'ff' is loaded; 2) an adaptor for the parallel backend (like 'doMPI', 'doSNOW', 'doMC') is loaded and registered.

If only the 'ff' package is available and loaded (in addition to the caller package - 'oligo' or 'crlmm'), these methods will allow the user to analyze datasets that would not fit in RAM at the expense of performance.

In the situations above (large datasets and cluster), oligo/crlmm uses the options ocSamples and ocProbesets to limit the amount of RAM used by the machine(s). For example, if ocSamples is set to 100, steps like background correction and normalization process (in RAM) 100 samples simultaneously on each compute node. If ocProbesets is set to 10K, then summarization processes 10K probesets at a time on each machine.

Warning

In both scenarios (large dataset and/or cluster use), there is a penalty in performance because data are written to disk (to either minimize memory footprint or share data across compute nodes).

Author(s)

Benilton Carvalho

Examples

if(require(doMC)) {
       registerDoMC()
       ## tasks like summarize()
   }

Description

An example instance of the oligoSnpSet class

Usage

data(oligoSetExample)

Source

Created from the simulated locusLevelData provided in this package.

See Also

locusLevelData

Examples

## Not run: 
		## 'oligoSetExample' created by the following
		data(locusLevelData)
		oligoSet <- new("oligoSnpSet",
				copyNumber=integerMatrix(log2(locusLevelData[["copynumber"]]/100), 100),
				call=locusLevelData[["genotypes"]],
				callProbability=locusLevelData[["crlmmConfidence"]],
				annotation=locusLevelData[["platform"]],
				genome="hg19")
		oligoSet <- oligoSet[!is.na(chromosome(oligoSet)), ]
		oligoSet <- oligoSet[chromosome(oligoSet) < 3, ]
	
## End(Not run)
	data(oligoSetExample)
	oligoSet

Description

Methods for oligoSnpSet class

Methods

In the following code, object is an instance of the oligoSnpSet class.

new("oligoSnpSet", ...): Instantiates an object of class oligoSnpSet. The assayData elements of the oligoSnpSet class can include matrices of genotype calls, confidence scores for the genotype calls, B allele frequencies, absolute or relative copy number, and confidence scores for the copy number estimates. Each matrix should be coerced to an integer scale prior to assignment to the oligoSnpSet object. Validity methods defined for the class will fail if the matrices are not integers. See examples for additional details.

baf(object): Accessor for integer representation of the B allele frequencies. The value returned by this method can be divided by 1000 to obtain B allele frequencies on the original [0,1] scale.

baf(object) <- value: Assign an integer representation of the B allele frequencies to the 'baf' element of the assayData slot. value must be a matrix of integers. See the examples for help converting BAFs to a matrix of integers.

Description

Checks if oligo/crlmm can use parallel resources (needs ff and snow package, in addition to options(cluster=makeCluster(...)).

Usage

parStatus()

Value

logical

Author(s)

Benilton S Carvalho

Description

This function checks if a given package is available on BioConductor and installs it, in case it is.

Usage

pdPkgFromBioC(pkgname, lib = .libPaths()[1], verbose = TRUE)

Arguments

Details

Internet connection required.

Value

Logical: TRUE if package was found, downloaded and installed; FALSE otherwise.

Author(s)

Benilton Carvalho

See Also

download.packages

Examples

## Not run: 
pdPkgFromBioC("pd.mapping50k.xba240")

## End(Not run)

Description

Platform Information

Methods

object = "FeatureSet"

platform information

Description

This method will return the fragment length for PM probes.

Methods

object = "AffySNPPDInfo"

On AffySNPPDInfo objects, it will return the fragment length that contains the SNP in question.

Description

Methods for function position in package oligoClasses

Methods

The methods for position extracts the physical position stored as an integer for each marker in a eSet-derived class or a AnnotatedDataFrame-derived class.

signature(object = "AnnotatedDataFrame")

Accessor for physical position.

signature(object = "eSet")

If 'position' is included in fvarLabels(object), the physical position will be returned. Otherwise, an error is thrown.

signature(object = "GenomeAnnotatedDataFrame")

Accessor for physical position. If annotation was not available due to a missing or non-existent annotation package, the value returned by the accessor will be a vector of zero's.

Description

This function checkes the existence of a given package and loads it if available. If the package is not available, the function checks its availability on BioConductor, downloads it and installs it.

Usage

requireAnnotation(pkgname, lib=.libPaths()[1], verbose = TRUE)

Arguments

Value

Logical: TRUE if package is available or FALSE if package unavailable for download.

Author(s)

Benilton Carvalho

See Also

install.packages

Examples

## Not run: 
requirePackage("pd.mapping50k.xba240")

## End(Not run)

Description

Package loaders for clusters.

Usage

requireClusterPkgSet(packages)
requireClusterPkg(pkg, character.only)

Arguments

Details

requireClusterPkgSet applies require for a set of packages on the cluster nodes.

requireClusterPkg applies require for *ONE* package on the cluster nodes and accepts every argument taken by require.

Value

Logical.

Author(s)

Benilton S Carvalho

See Also

require

Description

Returns sample names for FeatureSet-like objects.

Methods

object = "FeatureSet"

Sample names

Description

Example of SnpCnvQSet object.

Usage

data(scqsExample)

Format

Object belongs to SnpCnvQSet class.

Examples

data(scqsExample)
class(scqsExample)

Description

Tools to simplify management of clusters via 'snow' package and large dataset handling through the 'bigmemory' package.

Usage

setCluster(...)
getCluster()
delCluster()

Arguments

Details

Some methods in the oligo/crlmm packages, like backgroundCorrect, normalize, summarize and rma can use a cluster (set through 'snow' package). The use of cluster features is conditioned on the availability of the 'bigmemory' (used to provide shared objects across compute nodes) and 'snow' packages.

To use a cluster, 'oligo/crlmm' checks for three requirements: 1) 'ff' is loaded; 2) 'snow' is loaded; and 3) the 'cluster' option is set (e.g., via options(cluster=makeCluster(...)) or setCluster(...)).

If only the 'ff' package is available and loaded (in addition to the caller package - 'oligo' or 'crlmm'), these methods will allow the user to analyze datasets that would not fit in RAM at the expense of performance.

In the situations above (large datasets and cluster), oligo/crlmm uses the options ocSamples and ocProbesets to limit the amount of RAM used by the machine(s). For example, if ocSamples is set to 100, steps like background correction and normalization process (in RAM) 100 samples simultaneously on each compute node. If ocProbesets is set to 10K, then summarization processes 10K probesets at a time on each machine.

Warning

In both scenarios (large dataset and/or cluster use), there is a penalty in performance because data are written to disk (to either minimize memory footprint or share data across compute nodes).

Author(s)

Benilton Carvalho

Description

Example of SnpFeatureSet object.

Usage

data(sfsExample)

Format

Object belongs to SnpFeatureSet class

Examples

data(sfsExample)
class(sfsExample)

Description

Utility functions for accessing data in SnpSet objects.

Usage

calls(object)
calls(object) <- value
confs(object, transform=TRUE)
confs(object) <- value

Arguments

Details

calls returns the genotype calls. CRLMM stores genotype calls as integers (1 - AA; 2 - AB; 3 - BB).

confs returns the confidences associated with the genotype calls. The current implementation of CRLMM stores the confidences as integers to save memory on disk by using the transformation:

round(-1000*log2(1-p)),

where 'p' is the posterior probability of the call. confs is a convenience function that transforms the integer representation back to a probability. Note that if the assayData elements of the SnpSet objects are ff_matrix or ffdf, the confs function will return a warning. For such objects, one should first subset the ff object and coerce to a matrix, then apply the above conversion. The function snpCallProbability for the callProbability slot of SnpSet objects. See the examples below.

checkOrder checks whether the object is ordered by chromosome and physical position, evaluating to TRUE or FALSE.

Note

Note that the replacement method for confs<- expects a matrix of probabilities and will automatically convert the probabilities to an integer representation. See details for the conversion.

The accessor snpCallProbability is an accessor for the 'callProbability' element of the assayData. The name can be misleading, however, as the accessor will not return a probability if the call probabilities are represented as integers.

See Also

The helper functions p2i converts probabilities to integers and i2p converts integers to probabilities.

See order and checkOrder.

Examples

theCalls <- matrix(sample(1:3, 20, rep=TRUE), nc=2)
	p <- matrix(runif(20), nc=2)
	integerRepresentation <- matrix(as.integer(round(-1000*log(1-p))), 10, 2)
	obj <- new("SnpSet2", call=theCalls, callProbability=integerRepresentation)
	calls(obj)
        confs(obj)  ## coerces to probability scale
        int <- Biobase::snpCallProbability(obj) ## not necessarily a probability
        p3 <- i2p(int)  ## to convert back to a probability

Description

A container for genotype calls and confidence scores. Similar to the SnpSet class in Biobase, but SnpSet2 extends gSet directly whereas SnpSet extends eSet. Useful properties of gSet include the genome slot and the GenomeAnnotatedDataFrame.

Objects from the Class

Objects can be created by calls of the form new("SnpSet2", assayData, phenoData, featureData, experimentData, annotation, protocolData, call, callProbability, genome, ...).

Slots

genome:

Object of class "character" indicating the UCSC genome build. Supported builds are 'hg18' and 'hg19'.

assayData:

Object of class "AssayData".

phenoData:

Object of class "AnnotatedDataFrame".

featureData:

Object of class "AnnotatedDataFrame".

experimentData:

Object of class "MIAxE".

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "gSet", directly. Class "eSet", by class "gSet", distance 2. Class "VersionedBiobase", by class "gSet", distance 3. Class "Versioned", by class "gSet", distance 4.

Accessors

The argument object for the following methods is an instance of the SnpSet2 class.

calls(object): calls(object) <- value:

Gets or sets the genotype calls. value can be a matrix or a ff_matrix.

confs(object): confs(object) <- value:

Gets or sets the genotype confidence scores. value can be a matrix or a ff_matrix.

snpCall(object): snpCallProbability(object) <- value:

Gets or sets the genotype confidence scores.

Author(s)

R. Scharpf

See Also

SnpSet

Examples

showClass("SnpSet2")
new("SnpSet2")

Description

A class to store locus-level summaries of the quantile normalized intensities, genotype calls, and genotype confidence scores

Objects from the Class

new("SnpSuperSet", allelea=alleleA, alleleB=alleleB, call=call, callProbability, ...).

Slots

assayData:

Object of class "AssayData" ~~

phenoData:

Object of class "AnnotatedDataFrame" ~~

featureData:

Object of class "AnnotatedDataFrame" ~~

experimentData:

Object of class "MIAME" ~~

annotation:

Object of class "character" ~~

protocolData:

Object of class "AnnotatedDataFrame" ~~

.__classVersion__:

Object of class "Versions" ~~

Extends

Class "AlleleSet", directly. Class "SnpSet", directly. Class "eSet", by class "AlleleSet", distance 2. Class "VersionedBiobase", by class "AlleleSet", distance 3. Class "Versioned", by class "AlleleSet", distance 4.

Methods

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

Author(s)

R. Scharpf

See Also

AlleleSet

Examples

showClass("SnpSuperSet")
## empty object from the class
x <- new("matrix")
new("SnpSuperSet", alleleA=x, alleleB=x, call=x, callProbability=x)

Description

Tools to distribute objects across nodes or by length.

Usage

splitIndicesByLength(x, lg, balance=FALSE)
splitIndicesByNode(x)

Arguments

Details

splitIndicesByLength splits x in groups of length lg.

splitIndicesByNode splits x in N groups (where N is the number of compute nodes available).

Value

List.

Author(s)

Benilton S Carvalho

See Also

split

Examples

x <- 1:100
splitIndicesByLength(x, 8)
splitIndicesByLength(x, 8, balance=TRUE)
splitIndicesByNode(x)

Description

Example of SnpQSet instance.

Usage

data(sqsExample)

Format

Belongs to SnpQSet class.

Examples

data(sqsExample)
class(sqsExample)

Description

Methods for RangedSummarizedExperiment.

Usage

## S4 method for signature 'RangedSummarizedExperiment'
baf(object)
## S4 method for signature 'RangedSummarizedExperiment'
chromosome(object,...)
## S4 method for signature 'RangedSummarizedExperiment'
isSnp(object, ...)
## S4 method for signature 'RangedSummarizedExperiment'
lrr(object)

Arguments

Details

baf and lrr are accessors for the B allele frequencies and log R ratio assays (matrices or arrays), respectively,

chromosome returns the seqnames of the rowRanges.

isSnp returns a logical vector for each marker in rowRanges indicating whether the marker targets a SNP (nonpolymorphic regions are FALSE).

See Also

RangedSummarizedExperiment