exprSet {Biobase}R Documentation

Class for Microarray Data and Methods for Processing Them

Description

This is a class representation for microarray data

Extends

Directly extends class annotatedDataset.

Creating Objects

new('exprSet', exprs = [exprMatrix], se.exprs = [exprMatrix], phenoData = [phenoData], annotation = [character], description = [characterORMIAME], notes = [character])

Slots

Derived from annotatedDataset:

reporterInfo
class:data.frameOrNULL
phenoData:
Object of class 'phenoData' containing the patient (or case) level data. The columns of the pData slot of this entity represent variables and the rows represent patients or cases.

Introduced in exprSet:

exprs:
Object of class 'exprMatrix'. The observed expression levels. This is a matrix with columns representing patients or cases and rows representing genes.
se.exprs:
Object of class 'exprMatrix'. This is a matrix of the same dimensions as exprs which contains standard error estimates for the estimated expression levels.
annotation
A character string identifying the annotation that may be used for the exprSet instance.
description:
Object of class 'characterORMIAME'. For compatibility with previous version of this class description can also be a 'character'. The clase characterOrMIAME has been defined just for this.
notes:
Object of class 'character' of explanatory text

Methods

Derived from annotatedDataset:

$(exprSet) and $(exprSet, value)<-
An old-style method. It is pData(eset)[[as.character(val)]] which does not quite have the right semantics but it is close. This operator extracts the named component of the pData slot in phenoData.
[[(index) and [[(index, value)<-:
see annotatedDataset
phenoData(exprSet) and phenoData(exprSet, value)<-
see annotatedDataset
reporterInfo(exprSet) and reporterInfo(exprSet, value)<-
see annotatedDataset
pData(exprSet) and pData(exprSet, value)<-
see annotatedDataset
varLabels(exprSet)
see annotatedDataset

Class-specific methods:

update2MIAME(exprSet):
Converts exprSets from previous versions, that have a character in description to an object that has an instance of the class MIAME in the description slot. The old description is stored in the title slot. If the object already has a MIAME description the same object is returned.
assayData(exprSet):
Method to access exprs slot
exprs(exprSet) and exprs(exprSet)<-:
Methods to access/update exprs slot
se.exprs(exprSet) and se.exprs(exprSet)<-:
Methods to access/update se.exprs slot
description(exprSet) and description(exprSet, value)<-:
Methods to access/update description slot
annotation(exprSet) and annotation(exprSet, value)<-:
Methods to access/update annotation slot
notes(exprSet) and notes(exprSet, value)<-:
Methods to access/update notes slot
abstract(exprSet):
Not documented: function(object) abstract(description(object))
sampleNames(exprSet) and sampleNames(exprSet, value)<-:
Methods to access/update dimnames of the exprs slot
geneNames(exprSet) and geneNames(exprSet, value)<-:
Methods to access/update row.names of the exprs slot - gene names
write.exprs(exprSet,...):
Writes the expression levels to file. It takes the same arguments as write.table. If called with no arguments it is equivalent to write.table(exprs(exprSet),file="tmp.txt",quote=FALSE,sep="\t").
exprs2excel(exprSet,...):
Writes the expression levels to csv file. This file will open nicely in excel. It takes the same arguments as write.table. If called with no arguments it is equivalent to write.table(exprs(exprSet),file="tmp.csv", sep = ",", col.names = NA).
as.data.frame.exprSet(exprSet, row.names = NA, optional = NA):
Converts exprSet into a data.frame. In the return value, the first column is called exprs and contains the values returned by the method exprs(). The second column is called genenames and contains the values returned by the method geneNames(). The other columns will depend on the contents of the phenoData slot.

Iterator-series methods:
This is a set of methods to iterate over different types of objects. The behaviour of the methods is similar to that of the apply family.

iter(exprSet, missing, function):
An iterator over genes. Returns the result of applying function to the matrix of expressions on margin 1 (see apply)
iter(exprSet, missing, list):
A multi-iterator over genes. Concatenates result of applying each function in the list list in a matrix (assumes result of each function evaluation is a scalar).
iter(exprSet, character, function):
An iterator over genes. function is assumed to have arguments x and y; the pData element named by covlab will be bound to x, the gene expression values will be iteratively bound to y

Split-series methods:

split(exprSet, factor):
See method for vector
split(exprSet, vector):
Splits the exprSet. The returned value is a list, each component of which is an exprSet. If the length of vector is a divisor of the number of rows of the phenoData data frame then the split is made on this.

Standard generic methods:

show(exprSet):
Renders information about the exprSet in a concise way on stdout.
[(exprSet):
A subset operator. Ensures that both exprs and phenoData are subset properly.

See Also

MIAME, annotatedDataset, phenoData, class:exprMatrix, class:characterORMIAME, read.exprSet, esApply

Examples

  data(geneData)
  data(geneCov)
  covdesc<- list("Covariate 1", "Covariate 2", "Covariate 3")
  names(covdesc) <- names(geneCov)
  pdata <- new("phenoData", pData=geneCov, varLabels=covdesc)
  pdata[1,]
  pdata[,2]

  eset <- new("exprSet", exprs=geneData, phenoData=pdata)
  eset
  eset[,1:10]
  eset[,1]
  eset[1,]
  eset[1,1]
  eset[1:100,]
  eset[1:44,c(2,4,6)]
  Means <- iter(eset, f=mean)

  chkdich <- function(x) if(length(unique(x))!=2) stop("x not dichotomous")
  mytt <- function(x,y) {
     chkdich(x)
     d <- split(y,x)
     t.test(d[[1]],d[[2]])$p.val
  }

  Tpvals <- iter(eset, "cov1", mytt )

  sp1 <- split(eset, c(1,2))
  sp2 <- split(eset, c(rep(1,6), rep(2,7)))

  sampleNames(eset)
  sampleNames(eset) <- letters

  # as.data.frame.exprSet - example
  data(eset)
  sd.genes <- esApply(eset, 1, sd)
  dataf <- as.data.frame(eset)
  dataf <- cbind(dataf, sd.genes=rep(unname(sd.genes), length=nrow(dataf)))
  coplot(sd.genes ~ exprs | cov1+cov2, data=dataf)


[Package Biobase version 1.8.0 Index]