betweenLaneNormalization-methods {EDASeq} | R Documentation

## Methods for Function `betweenLaneNormalization` in Package EDASeq

### Description

Between-lane normalization for sequencing depth and possibly other distributional differences between lanes.

### Usage

    betweenLaneNormalization(x, which=c("median","upper","full"), offset=FALSE, round=TRUE)

### Arguments

`x` | A numeric matrix representing the counts or a `SeqExpressionSet` object.
`which` | Method used to normalized. See the details section and the reference below for details.
`offset` | Should the normalized value be returned as an offset leaving the original counts unchanged?
`round` | If TRUE the normalization returns rounded values (pseudo-counts). Ignored if offset=TRUE.

### Details

This method implements three normalizations described in Bullard et al. (2010). The methods are:

`median`:

  a scaling normalization that forces the median of each lane to be the same.

`upper`:

  the same but with the upper quartile.

`full`:

  a non linear full quantile normalization, in the spirit of the one used in microarrays.

### Methods

`signature(x = "matrix")`

  It returns a matrix with the normalized counts if `offset=FALSE` or with the offset if `offset=TRUE`.

`signature(x = "SeqExpressionSet")`

  It returns a `linkS4class{SeqExpressionSet}` with the normalized counts in the `normalizedCounts` slot and with the offset in the `offset` slot (if `offset=TRUE`).

### Author(s)

Davide Risso.

### References

J. H. Bullard, E. A. Purdom, K. D. Hansen and S. Dudoit (2010). Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinformatics Vol. 11, Article 94.

D. Risso, K. Schwartz, G. Sherlock and S. Dudoit (2011). GC-Content Normalization for RNA-Seq Data. Manuscript in Preparation.

### Examples

    library(yeastRNASeq)
    data(geneLevelData)
    data(yeastGC)

    sub <- intersect(rownames(geneLevelData), names(yeastGC))

    mat <- as.matrix(geneLevelData[sub, ])

    data <- newSeqExpressionSet(mat,
                                phenoData=AnnotatedDataFrame(
                                          data.frame(conditions=factor(c("mut", "mut", "wt", "wt")),
                                                     row.names=colnames(geneLevelData))),
                                featureData=AnnotatedDataFrame(data.frame(gc=yeastGC[sub])))

    norm <- betweenLaneNormalization(data, which="full", offset=FALSE)

[Package EDASeq version 2.18.0 Index]