SeqExpressionSet-class: "SeqExpressionSet" class for collections of short reads
In EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq

Description Objects from the Class Extends Slots Methods Author(s) See Also Examples

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

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

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

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}

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.

Davide Risso <risso.davide@gmail.com>

eSet, newSeqExpressionSet, biasPlot, withinLaneNormalization, betweenLaneNormalization

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)