ExpressionSet {Biobase} | R Documentation

## 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.

### Extends

Directly extends class `eSet`.

### Creating Objects

`new("ExpressionSet")`

`new("ExpressionSet", phenoData = new("AnnotatedDataFrame"), featureData = new("AnnotatedDataFrame"), experimentData = new("MIAME"), annotation = character(0), exprs = new("matrix"))`

This creates an `ExpressionSet` with `assayData` implicitly created to contain `exprs`. Additional named matrix arguments with the same dimensions as `exprs` are added to `assayData`; the row and column names of these additional matricies should match those of `exprs`.

`new("ExpressionSet", assayData = assayDataNew(exprs=new("matrix")), phenoData = new("AnnotatedDataFrame"), featureData = new("AnnotatedDataFrame"), experimentData = new("MIAME"), annotation = character(0))`

This creates an `ExpressionSet` with `assayData` provided explicitly. In this form, the only required named argument is `assayData`.

`as([exprSet],"ExpressionSet")`

`ExpressionSet` instances are usually created through `new("ExpressionSet", ...)`. Usually the arguments to `new` include `exprs` (a matrix of expression data, with features corresponding to rows and samples to columns), `phenoData`, `featureData`, `experimentData`, and `annotation`. `phenoData`, `featureData`, `experimentData`, and `annotation` can be missing, in which case they are assigned default values.

### 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 represening features (e.g., reporters) 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`

### 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`
`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 outaded versions of `ExpressionSet` to their current definiton. 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`

### Author(s)

Biocore team

### See Also

`eSet-class`, `ExpressionSet-class`.

### Examples

    # create an instance of ExpressionSet
    new("ExpressionSet")

    new("ExpressionSet",
        exprs=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]
    phenoData(sample.ExpressionSet)
    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)

[Package Biobase version 2.2.2 Index]