EDASeq (version 2.6.2)

betweenLaneNormalization-methods: 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.

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).

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.

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)