exprSet {Biobase} | R Documentation |
This is a class representation for microarray data
Directly extends class annotatedDataset
.
new('exprSet', exprs = [exprMatrix], se.exprs = [exprMatrix], phenoData = [phenoData], annotation = [character], description = [characterORMIAME], notes = [character])
Derived from annotatedDataset
:
reporterInfo
data.frameOrNULL
phenoData
:
Introduced in exprSet
:
exprs
:se.exprs
:exprs
which contains standard error estimates for the estimated expression levels. annotation
exprSet
instance.description
:characterOrMIAME
has been defined just for this.notes
:
Derived from annotatedDataset
:
$(exprSet)
and $(exprSet, value)<-
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)<-
:annotatedDataset
phenoData(exprSet)
and phenoData(exprSet, value)<-
annotatedDataset
reporterInfo(exprSet)
and reporterInfo(exprSet, value)<-
annotatedDataset
pData(exprSet)
and pData(exprSet, value)<-
annotatedDataset
varLabels(exprSet)
annotatedDataset
Class-specific methods:
update2MIAME(exprSet)
:exprSet
s 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)
:exprs
slotexprs(exprSet)
and exprs(exprSet)<-
:exprs
slot se.exprs(exprSet)
and se.exprs(exprSet)<-
:se.exprs
slot description(exprSet)
and description(exprSet, value)<-
:description
slot annotation(exprSet)
and annotation(exprSet, value)<-
:annotation
slotnotes(exprSet)
and notes(exprSet, value)<-
:notes
slot abstract(exprSet)
:function(object) abstract(description(object))
sampleNames(exprSet)
and sampleNames(exprSet, value)<-
:dimnames
of the exprs
slot geneNames(exprSet)
and geneNames(exprSet, value)<-
:row.names
of the exprs
slot - gene names write.exprs(exprSet,...)
:write.table
. If called with no arguments it is equivalent to write.table(exprs(exprSet),file="tmp.txt",quote=FALSE,sep="\t")
.exprs2excel(exprSet,...)
: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)
: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)
:function
to the matrix of expressions on margin 1 (see apply
)iter(exprSet, missing, list)
:list
in a matrix (assumes result of each function evaluation is a scalar).iter(exprSet, character, function)
: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 ySplit-series methods:
split(exprSet, factor)
:vector
split(exprSet, vector)
: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)
:[(exprSet)
:exprs
and phenoData
are subset properly.
MIAME
, annotatedDataset
, phenoData
, class:exprMatrix
, class:characterORMIAME
, read.exprSet
, esApply
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)