SeqExpressionSet-class {EDASeq} | R Documentation

## "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.

### Author(s)

Davide Risso <risso.davide@gmail.com>

### 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)

[Package EDASeq version 2.20.0 Index]