| 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:
reporterInfodata.frameOrNULLphenoData:
Introduced in exprSet:
exprs:se.exprs:exprs which contains standard error estimates for the estimated expression levels. annotationexprSet 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)<-:annotatedDatasetphenoData(exprSet) and phenoData(exprSet, value)<-annotatedDatasetreporterInfo(exprSet) and reporterInfo(exprSet, value)<-annotatedDatasetpData(exprSet) and pData(exprSet, value)<-annotatedDatasetvarLabels(exprSet)annotatedDatasetClass-specific methods:
update2MIAME(exprSet):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):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):vectorsplit(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)