class-IcaSet: Class to Contain and Describe an ICA decomposition of...

Description Extends Creating Objects Slots Methods Author(s) See Also Examples

Description

Container for high-throughput data and results of ICA decomposition obtained on these data. IcaSet class is derived from eSet, and requires a matrix named dat as assayData member.

Extends

Directly extends class eSet.

Creating Objects

new("IcaSet")

new("IcaSet", annotation = character(0), experimentData = new("MIAME"), featureData = new("AnnotatedDataFrame"), phenoData = new("AnnotatedDataFrame"), protocolData = phenoData[,integer(0)], dat = new("matrix"), A=new("data.frame"), S=new("data.frame"), ...)

This creates an IcaSet with assayData implicitly created to contain dat.

new("IcaSet", annotation = character(0), assayData = assayDataNew(dat=new("matrix")), experimentData = new("MIAME"), featureData = new("AnnotatedDataFrame"), phenoData = new("AnnotatedDataFrame"), protocolData = phenoData[,integer(0)], A=new("data.frame"), S=new("data.frame"), ...)

This creates an IcaSet with assayData provided explicitly.

IcaSet instances are usually created through new("IcaSet", ...). Usually the arguments to new include dat ('features x samples', e.g a matrix of expression data), phenoData ('samples x annotations', a matrix of sample annotations), S the Source matrix of the ICA decomposition ('features x comp'), A the Mixing matrix of the ICA decomposition ('samples x comp'), annotation the annotation package, typeID the description of the feature and gene IDs.

The other attributes can be missing, in which case they are assigned default values.

The function buildIcaSet is a more convenient way to create IcaSet instances, and allows to automatically annotate the features.

Slots

Inherited from eSet:

annotation:

See eSet

assayData:

Contains matrices with equal dimensions, and with column number equal to nrow(phenoData). assayData must contain a matrix dat with rows representing features (e.g., reporters) and columns representing samples. Class:AssayData-class

experimentData:

See eSet

featureData:

See eSet

phenoData:

See eSet

protocolData:

See eSet

Specific slot:

organism:

Contains the name of the species. Currently only Human ("Human" or "Homo sapiens") and Mouse ("Mouse" or "Mus musculus") are supported. Only used when chipManu="illumina"

mart:

An output of useMart of package biomaRt. Only useful if no annotation package is available for argument icaSet.

datByGene:

Data.frame containing the data dat where features have been replaced by their annotations (e.g, gene IDs). Rows represent annotations of the features (e.g., gene IDs) and columns represent samples.

A:

The mixing matrix of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals nrow(phenoData) (dimension: 'samples x comp').

S:

The source matrix of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals nrow(assayData) (dimension: 'features x comp').

SByGene:

The matrix Source of the ICA decomposition, contained in a data.frame whose column number equals the number of components and row number equals nrow(datByGene) (dimension: 'annotatedFeatures x comp').

compNames:

A vector of component labels with length equal to the number of component.

indComp:

A vector of component indices with length equal to the number of component.

witGenes:

A vector of gene IDs with length equal to the number of component.

chipManu:

The manufacturer of the technology the data originates from. Useful for the annotation of the features when data originates from an _illumina_ microarray.

chipVersion:

The version of the chip, only useful for when chipManu="illumina"

refSamples:

A vector of sample IDs including the reference samples, e.g the "normal" samples. Must be included in sampleNames(object), i.e in colnames(dat).

typeID:

A vector of characters providing the annotation IDs. It includes three elements:

geneID_annotation

the IDs from the package to be used to annotate the features into genes. It will be used to fill the attributes datByGene and SByGene of the icaSet. It must match one of the objects the corresponding package supports (you can access the list of objects by typing ls("package:packagename")). If no annotation package is provided, this element is not useful.

geneID_biomart

the type of gene IDs, as available in listFilters(mart); where mart is specified as described in useMart. If you have directly built the IcaSet at the gene level (i.e if no annotation package is used), featureID_biomart and geneID_biomart will be identical.

featureID_biomart

the type of feature IDs, as available in listFilters(mart); where mart is specified as described in function useMart. Not useful if you work at the gene level.

Methods

Class-specific methods.

getComp(IcaSet, ind, level=c("features","genes"))

Given a component index, extract the corresponding sample contribution values from A, and the feature (level="features") or gene (level="genes") projections from S. Returns a list with two elements: contrib the sample contributions and proj the feature or gene projections.

Access and set any slot specific to IcaSet:

slotName(IcaSet), and slotName(IcaSet)<-:

Accessing and setting any slot of name slotName contained in an IcaSet object.

IcaSet["slotName"], and IcaSet["slotName"]<-:

Accessing and setting any slot of name slotName contained in an IcaSet object.

Most used accessors and settors:

A(IcaSet), and A(IcaSet)<-:

Accessing and setting Mixing matrix A.

S(IcaSet), and S(IcaSet)<-:

Accessing and setting the data.frame Source S.

Slist(IcaSet):

Accessing the data.frame Source as a list where names are preserved.

SByGene(IcaSet), and SByGene(IcaSet)<-:

Accessing and setting the _annotated_ data.frame Source SByGene.

SlistByGene(IcaSet):

Accessing the _annotated_ Source matrix as a list where names are preserved.

organism(IcaSet), organism(IcaSet,characte)<-

Access and set value in the organism slot.

dat(IcaSet), dat(IcaSet,matrix)<-

Access and set elements named dat in the AssayData-class slot.

Derived from eSet:

pData(IcaSet), pData(IcaSet,value)<-:

See eSet

assayData(IcaSet):

See eSet

sampleNames(IcaSet) and sampleNames(IcaSet)<-:

See eSet

featureNames(IcaSet), featureNames(IcaSet, value)<-:

See eSet

dims(IcaSet):

See eSet

phenoData(IcaSet), phenoData(IcaSet,value)<-:

See eSet

varLabels(IcaSet), varLabels(IcaSet, value)<-:

See eSet

varMetadata(IcaSet), varMetadata(IcaSet,value)<-:

See eSet

varMetadata(IcaSet), varMetadata(IcaSet,value)

See eSet

experimentData(IcaSet),experimentData(IcaSet,value)<-:

See eSet

pubMedIds(IcaSet), pubMedIds(IcaSet,value)

See eSet

abstract(IcaSet):

See eSet

annotation(IcaSet), annotation(IcaSet,value)<-

See eSet

protocolData(IcaSet), protocolData(IcaSet,value)<-

See eSet

combine(IcaSet,IcaSet):

See eSet

storageMode(IcaSet), storageMode(IcaSet,character)<-:

See eSet

Standard generic methods:

initialize(IcaSet):

Object instantiation, used by new; not to be called directly by the user.

validObject(IcaSet):

Validity-checking method, ensuring that dat is a member of assayData, and that the number of features, genes, samples, and components are consistent across all the attributes of the IcaSet object. checkValidity(IcaSet) imposes this validity check, and the validity checks of eSet.

IcaSet[slotName], IcaSet[slotName]<-:

Accessing and setting any slot of name slotName contained in an IcaSet object.

IcaSet[i, j, k]:

Extract object of class "IcaSet" for features or genes with names i, samples with names or indices j, and components with names or indices k.

makeDataPackage(object, author, email, packageName, packageVersion, license, biocViews, filePath, description=paste(abstract(object), collapse="\n\n"), ...)

Create a data package based on an IcaSet object. See makeDataPackage.

show(IcaSet):

See eSet

dim(IcaSet), ncol:

See eSet

IcaSet[(index)]:

See eSet

IcaSet$, IcaSet$<-:

See eSet

IcaSet[[i]], IcaSet[[i]]<-:

See eSet

Author(s)

Anne Biton

See Also

eSet-class, buildIcaSet, IcaSet-class, MineICAParams-class.

Examples

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# create an instance of IcaSet
new("IcaSet")
dat <- matrix(runif(100000), nrow=1000, ncol=100)
rownames(dat) <- 1:nrow(dat)
new("IcaSet",
    dat=dat, 
    A=as.data.frame(matrix(runif(1000), nrow=100, ncol=10)),
    S=as.data.frame(matrix(runif(10000), nrow=1000, ncol=10), row.names = 1:nrow(dat)))

Bioconductor-mirror/MineICA documentation built on May 29, 2017, 8:30 a.m.