ExpressionSet
class is derived from
eSet
, and requires a matrix named exprs
as
assayData member.
## 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
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()
.
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
.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
.MIAME
instance with meta-data
(e.g., the lab and resulting publications from the analysis) about
the experiment.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.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
.new("ExpressionSet", ...)
and available for classes that extend
ExpressionSet
.
eSet
.ExpressionSet
instances are usually created through
ExpressionSet()
. eSet
:
assayData
: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
:eSet
featureData
:eSet
experimentData
:eSet
annotation
:eSet
protocolData
:eSet
as(exprSet,"ExpressionSet")
exprSet-class
to ExpressionSet
as(object,"data.frame")
ExpressionSet-class
to data.frame
by
transposing the expression matrix and concatenating phenoData
exprs(ExpressionSet)
, exprs(ExpressionSet,matrix)<-
exprs
in the AssayData-class
slot.esApply(ExpressionSet, MARGIN, FUN,
...)
ExpressionSet
objects. See esApply
.write.exprs(ExpressionSet)
write.table
eSet
:
updateObject(object, ..., verbose=FALSE)
updateObject
and eSet
isCurrent(object)
isCurrent
isVersioned(object)
isVersioned
assayData(ExpressionSet)
:eSet
sampleNames(ExpressionSet)
and sampleNames(ExpressionSet)<-
:eSet
featureNames(ExpressionSet)
, featureNames(ExpressionSet, value)<-
:eSet
dims(ExpressionSet)
:eSet
phenoData(ExpressionSet)
, phenoData(ExpressionSet,value)<-
:eSet
varLabels(ExpressionSet)
, varLabels(ExpressionSet, value)<-
:eSet
varMetadata(ExpressionSet)
, varMetadata(ExpressionSet,value)<-
:eSet
pData(ExpressionSet)
, pData(ExpressionSet,value)<-
:eSet
varMetadata(ExpressionSet)
, varMetadata(ExpressionSet,value)
eSet
experimentData(ExpressionSet)
,experimentData(ExpressionSet,value)<-
:eSet
pubMedIds(ExpressionSet)
, pubMedIds(ExpressionSet,value)
eSet
abstract(ExpressionSet)
:eSet
annotation(ExpressionSet)
, annotation(ExpressionSet,value)<-
eSet
protocolData(ExpressionSet)
, protocolData(ExpressionSet,value)<-
eSet
combine(ExpressionSet,ExpressionSet)
:eSet
storageMode(ExpressionSet)
, storageMode(ExpressionSet,character)<-
:eSet
initialize(ExpressionSet)
:new
; not to be called directly by the user.updateObject(ExpressionSet)
:ExpressionSet
to their current definition. See
updateObject
, Versions-class
.validObject(ExpressionSet)
: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"), ...)
makeDataPackage
.as(exprSet,ExpressionSet)
:exprSet
to ExpressionSet
.as(eSet,ExpressionSet)
:eSet
portion of an object to ExpressionSet
.show(ExpressionSet)
eSet
dim(ExpressionSet)
, ncol
eSet
ExpressionSet[(index)
:eSet
ExpressionSet$
, ExpressionSet$<-
eSet
ExpressionSet[[i]]
, ExpressionSet[[i]]<-
eSet
eSet-class
, ExpressionSet-class
.
# create an instance of ExpressionSet
ExpressionSet()
ExpressionSet(assayData=matrix(runif(1000), nrow=100, ncol=10))
# update an existing ExpressionSet
data(sample.ExpressionSet)
updateObject(sample.ExpressionSet)
# information about assay and sample data
featureNames(sample.ExpressionSet)[1:10]
sampleNames(sample.ExpressionSet)[1:5]
experimentData(sample.ExpressionSet)
# subset: first 10 genes, samples 2, 4, and 10
expressionSet <- sample.ExpressionSet[1:10,c(2,4,10)]
# named features and their expression levels
subset <- expressionSet[c("AFFX-BioC-3_at","AFFX-BioDn-5_at"),]
exprs(subset)
# samples with above-average 'score' in phenoData
highScores <- expressionSet$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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