betweenLaneNormalization-methods: Methods for Function 'betweenLaneNormalization' in Package...
In EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq

Description Usage Arguments Details Methods Author(s) References Examples

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

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

`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.

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.

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).

Davide Risso.

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.

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)