ExpressionSet: Class to Contain and Describe High-Throughput Expression Level Assays.

Description

Container for high-throughput assays and experimental metadata. ExpressionSet class is derived from eSet, and requires a matrix named exprs as assayData member.

Usage

## Instance creation
ExpressionSet(assayData, phenoData=annotatedDataFrameFrom(assayData, byrow=FALSE), featureData=annotatedDataFrameFrom(assayData, byrow=TRUE), experimentData=MIAME(), annotation=character(), protocolData=annotatedDataFrameFrom(assayData, byrow=FALSE), ...)
## Additional methods documented below

Arguments

assayData

A matrix of expression values, or an environment.

When assayData is a matrix, the rows represent probe sets (‘features’ in ExpressionSet parlance). Columns represent samples. When present, row names identify features and column names identify samples. Row and column names must be unique, and consistent with row names of featureData and phenoData, respectively. The assay data can be retrieved with exprs().

When assayData is an environment, it contains identically dimensioned matrices like that described in the previous paragraph. One of the elements of the environment must be named ‘exprs’; this element is returned with exprs().

phenoData

An optional AnnotatedDataFrame containing information about each sample. The number of rows in phenoData must match the number of columns in assayData. Row names of phenoData must match column names of the matrix / matricies in assayData.

featureData

An optional AnnotatedDataFrame containing information about each feature. The number of rows in featureData must match the number of rows in assayData. Row names of featureData must match row names of the matrix / matricies in assayData.

experimentData

An optional MIAME instance with meta-data (e.g., the lab and resulting publications from the analysis) about the experiment.

annotation

A character describing the platform on which the samples were assayed. This is often the name of a Bioconductor chip annotation package, which facilitated down-stream analysis.

protocolData

An optional AnnotatedDataFrame containing equipment-generated information about protocols. The number of rows and row names of protocolData must agree with the dimension and column names of assayData.

...

Additional arguments, passed to new("ExpressionSet", ...) and available for classes that extend ExpressionSet.

Extends

Directly extends class eSet.

Creating Objects

ExpressionSet instances are usually created through ExpressionSet().

Slots

Inherited from eSet:

assayData:: Contains matrices with equal dimensions, and with column number equal to nrow(phenoData). assayData must contain a matrix exprs with rows representing features (e.g., probe sets) and columns representing samples. Additional matrices of identical size (e.g., representing measurement errors) may also be included in assayData. Class:AssayData-class
phenoData:: See eSet
featureData:: See eSet
experimentData:: See eSet
annotation:: See eSet
protocolData:: See eSet

Methods

Class-specific methods.

as(exprSet,"ExpressionSet"): Coerce objects of exprSet-class to ExpressionSet
as(object,"data.frame"): Coerce objects of ExpressionSet-class to data.frame by transposing the expression matrix and concatenating phenoData
exprs(ExpressionSet), exprs(ExpressionSet,matrix)<-: Access and set elements named exprs in the AssayData-class slot.
esApply(ExpressionSet, MARGIN, FUN, ...): 'apply'-like function to conveniently operate on ExpressionSet objects. See esApply.
write.exprs(ExpressionSet): Write expression values to a text file. It takes the same arguments as write.table

Derived from eSet:

updateObject(object, ..., verbose=FALSE): Update instance to current version, if necessary. See updateObject and eSet
isCurrent(object): Determine whether version of object is current. See isCurrent
isVersioned(object): Determine whether object contains a 'version' string describing its structure . See isVersioned
assayData(ExpressionSet):: See eSet
sampleNames(ExpressionSet) and sampleNames(ExpressionSet)<-:: See eSet
featureNames(ExpressionSet), featureNames(ExpressionSet, value)<-:: See eSet
dims(ExpressionSet):: See eSet
phenoData(ExpressionSet), phenoData(ExpressionSet,value)<-:: See eSet
varLabels(ExpressionSet), varLabels(ExpressionSet, value)<-:: See eSet
varMetadata(ExpressionSet), varMetadata(ExpressionSet,value)<-:: See eSet
pData(ExpressionSet), pData(ExpressionSet,value)<-:: See eSet
varMetadata(ExpressionSet), varMetadata(ExpressionSet,value): See eSet
experimentData(ExpressionSet),experimentData(ExpressionSet,value)<-:: See eSet
pubMedIds(ExpressionSet), pubMedIds(ExpressionSet,value): See eSet
abstract(ExpressionSet):: See eSet
annotation(ExpressionSet), annotation(ExpressionSet,value)<-: See eSet
protocolData(ExpressionSet), protocolData(ExpressionSet,value)<-: See eSet
combine(ExpressionSet,ExpressionSet):: See eSet
storageMode(ExpressionSet), storageMode(ExpressionSet,character)<-:: See eSet

Standard generic methods:

initialize(ExpressionSet):: Object instantiation, used by new; not to be called directly by the user.
updateObject(ExpressionSet):: Update outdated versions of ExpressionSet to their current definition. See updateObject, Versions-class.
validObject(ExpressionSet):: Validity-checking method, ensuring that exprs is a member of assayData. checkValidity(ExpressionSet) imposes this validity check, and the validity checks of eSet.
makeDataPackage(object, author, email, packageName, packageVersion, license, biocViews, filePath, description=paste(abstract(object), collapse="\n\n"), ...): Create a data package based on an ExpressionSet object. See makeDataPackage.
as(exprSet,ExpressionSet):: Coerce exprSet to ExpressionSet.
as(eSet,ExpressionSet):: Coerce the eSet portion of an object to ExpressionSet.
show(ExpressionSet): See eSet
dim(ExpressionSet), ncol: See eSet
ExpressionSet[(index):: See eSet
ExpressionSet$, ExpressionSet$<-: See eSet
ExpressionSet[[i]], ExpressionSet[[i]]<-: See eSet

Examples

Run this code

# create an instance of ExpressionSet
ExpressionSet()

ExpressionSet(assayData=matrix(runif(1000), nrow=100, ncol=10))

# update an existing ExpressionSet
data(sample.ExpressionSet)
updateObject(sample.ExpressionSet)

# information about assay and sample data
featureNames(sample.ExpressionSet)[1:10]
sampleNames(sample.ExpressionSet)[1:5]
experimentData(sample.ExpressionSet)

# subset: first 10 genes, samples 2, 4, and 10
expressionSet <- sample.ExpressionSet[1:10,c(2,4,10)]

# named features and their expression levels
subset <- expressionSet[c("AFFX-BioC-3_at","AFFX-BioDn-5_at"),]
exprs(subset)

# samples with above-average 'score' in phenoData
highScores <- expressionSet$score > mean(expressionSet$score)
expressionSet[,highScores]

# (automatically) coerce to data.frame
lm(score~AFFX.BioDn.5_at + AFFX.BioC.3_at, data=subset)

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