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

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)

Results

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> library(EDASeq)
Loading required package: Biobase
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: ShortRead
Loading required package: BiocParallel
Loading required package: Biostrings
Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: XVector
Loading required package: Rsamtools
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: GenomicAlignments
Loading required package: SummarizedExperiment
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/EDASeq/betweenLaneNormalization-methods.Rd_%03d_medium.png", width=480, height=480)
> ### Name: betweenLaneNormalization-methods
> ### Title: Methods for Function 'betweenLaneNormalization' in Package
> ###   'EDASeq'
> ### Aliases: betweenLaneNormalization betweenLaneNormalization-methods
> ###   betweenLaneNormalization,matrix-method
> ###   betweenLaneNormalization,SeqExpressionSet-method
> ### Keywords: methods
> 
> ### ** 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)
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>