withinLaneNormalization-methods {EDASeq} | R Documentation

## Methods for Function `withinLaneNormalization` in Package EDASeq

### Description

Within-lane normalization for GC-content (or other lane-specific) bias.

### Usage

    withinLaneNormalization(x, y, which=c("loess","median","upper","full"), offset=FALSE, num.bins=10, round=TRUE)

### Arguments

`x` | A numeric matrix representing the counts or a `SeqExpressionSet` object.
`y` | A numeric vector representing the covariate to normalize for (if `x` is a matrix) or a character vector with the name of the covariate (if `x` is a `SeqExpressionSet` object). Usually it is the GC-content.
`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?
`num.bins` | The number of bins used to stratify the covariate for `median`, `upper` and `full` methods. Ignored if `loess`. See the reference for a discussion on the number of bins.
`round` | If TRUE the normalization returns rounded values (pseudo-counts). Ignored if offset=TRUE.

### Details

This method implements four normalizations described in Risso et al. (2011).

The `loess` normalization transforms the data by regressing the counts on `y` and subtracting the loess fit from the counts to remove the dependence.

The `median`, `upper` and `full` normalizations are based on the stratification of the genes based on `y`. Once the genes are stratified in `num.bins` strata, the methods work as follows.

`median`:

  scales the data to have the same median in each bin.

`upper`:

  the same but with the upper quartile.

`full`:

  forces the distribution of each stratum to be the same using a non linear full quantile normalization, in the spirit of the one used in microarrays.

### Methods

`signature(x = "matrix", y = "numeric")`

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

`signature(x = "SeqExpressionSet", y = "character")`

  It returns a `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

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 <- withinLaneNormalization(data, "gc", which="full", offset=FALSE)

[Package EDASeq version 2.20.0 Index]