EDASeq (version 2.6.2)

SeqExpressionSet-class: "SeqExpressionSet" class for collections of short reads

Description

This class represents a collection of digital expression data (usually counts from RNA-Seq technology) along with sample information.

Objects from the Class

Objects of this class can be created from a call to the newSeqExpressionSet constructor.

Extends

Class eSet, directly. Class VersionedBiobase, by class eSet, distance 2. Class Versioned, by class eSet, distance 3.

Slots

Inherited from eSet:

assayData

Contains matrices with equal dimensions, and with column number equal to nrow(phenoData).assayData must contain a matrix counts with rows represening features (e.g., genes) and columns representing samples. The optional matrices normalizedCounts and offset can be added to represent a normalization in terms of pseudo-counts or offset, respectively, to be used for subsequent analyses. See the vignette for details. Class: AssayData-class.

phenoData

Sample information. For compatibility with DESeq, there should be at least the column conditions. See eSet for details.

featureData

Feature information. It is recomended to include at least length and GC-content information. This slot is used for withinLaneNormalization. See eSet for details.

experimentData

See eSet

annotation

See eSet

protocolData

See link{eSet}

Methods

See eSet for inherited methods. Additional methods:

counts

signature(object="SeqExpressionSet"): returns the counts matrix.

counts<-

signature(object = "SeqExpressionSet"): method to replace the counts matrix.

normCounts

signature(object="SeqExpressionSet"): returns the normalizedCounts matrix.

normCounts<-

signature(object = "SeqExpressionSet"): method to replace the normalizedCounts matrix.

offst

signature(object = "SeqExpressionSet"): returns the offset matrix.

offst<-

signature(object = "SeqExpressionSet"): method to replace the offset slot.

boxplot

signature(x = "SeqExpressionSet"): produces a boxplot of the log counts.

meanVarPlot

signature(x = "SeqExpressionSet"): produces a smoothScatter plot of the mean variance relation. See meanVarPlot for details.

biasPlot

signature(x = "SeqExpressionSet", y = "character"): produces a plot of the lowess regression of the counts on some covariate of interest (usually GC-content or length). See biasPlot for details.

wihtinLaneNormalization

signature(x = "SeqExpressionSet", y = "missing"): within lane normalization for GC-content (or other lane specific) bias. See withinLaneNormalization for details.

betweenLaneNormalization

signature(x = "SeqExpressionSet"): between lane normalization for sequencing depth and possibly other distributional differences between lanes. See betweenLaneNormalization for details.

coerce

signature(from = "SeqExpressionSet", to = "CountDataSet"): coercion to DESeq class CountDataSet for compatibility with downstream analysis.

See Also

eSet, newSeqExpressionSet, biasPlot, withinLaneNormalization, betweenLaneNormalization

Examples

showMethods(class="SeqExpressionSet", where=getNamespace("EDASeq"))

counts <- matrix(data=0, nrow=100, ncol=4)
for(i in 1:4) {
counts[,i] <- rpois(100,lambda=50)
}
cond <- c(rep("A", 2), rep("B", 2))

data <- newSeqExpressionSet(counts, phenoData=AnnotatedDataFrame(data.frame(conditions=cond)))

head(counts(data))
boxplot(data, col=as.numeric(pData(data)[,1])+1)