normalize {EnrichmentBrowser} | R Documentation

## Normalization of microarray and RNA-seq expression data

### Description

This function wraps commonly used functionality from limma for microarray normalization and from EDASeq for RNA-seq normalization.

### Usage

    normalize(
      se,
      norm.method = "quantile",
      data.type = c(NA, "ma", "rseq"),
      filter.by.expr = TRUE
    )

### Arguments

`se` | An object of class `SummarizedExperiment`.
`norm.method` | Determines how the expression data should be normalized. For available microarray normalization methods see the man page of the limma function `normalizeBetweenArrays`. For available RNA-seq normalization methods see the man page of the EDASeq function `betweenLaneNormalization`. For microarray data, defaults to `'quantile'`, i.e. normalization is carried out so that quantiles between arrays/samples are equal. For RNA-seq data, defaults to `'upper'`, i.e. normalization is carried out so that quantiles between lanes/samples are equal up to the upper quartile. For RNA-seq data, this can also be `'vst'`, `'voom'`, or `'deseq2'` to invoke a variance-stabilizing transformation that allows statistical modeling as for microarry data. See details.
`data.type` | Expression data type. Use `'ma'` for microarray and `'rseq'` for RNA-seq data. If `NA`, the data type is automatically guessed: if the expression values in `se` are decimal (float) numbers, they are assumed to be microarray intensities; whole (integer) numbers are assumed to be RNA-seq read counts. Defaults to `NA`.
`filter.by.expr` | Logical. For RNA-seq data: include only genes with sufficiently large counts in the DE analysis? If TRUE, excludes genes not satisfying a minimum number of read counts across samples using the `filterByExpr` function from the edgeR package. Defaults to TRUE.

### Details

Normalization of high-throughput expression data is essential to make results within and between experiments comparable. Microarray (intensity measurements) and RNA-seq (read counts) data exhibit typically distinct features that need to be normalized for. For specific needs that deviate from standard normalizations, the user should always refer to more specific functions/packages. See also the limma's user guide http://www.bioconductor.org/packages/limma for definition and normalization of the different expression data types.

Microarray data is expected to be single-channel. For two-color arrays, it is expected that normalization within arrays has been already carried out, e.g. using `normalizeWithinArrays` from limma.

RNA-seq data is expected to be raw read counts. Please note that normalization for downstream DE analysis, e.g. with edgeR and DESeq2, is not ultimately necessary (and in some cases even discouraged) as many of these tools implement specific normalization approaches. See the vignette of EDASeq, edgeR, and DESeq2 for details.

Using `norm.method = "vst"` invokes a variance-stabilizing transformation (VST) for RNA-seq read count data. This accounts for differences in sequencing depth between samples and over-dispersion of read count data. The VST uses the `cpm` function implemented in the edgeR package to compute moderated log2 read counts. Using edgeR's estimate of the common dispersion phi, the `prior.count` parameter of the `cpm` function is chosen as 0.5 / phi as previously suggested (Harrison, 2015).

### Value

An object of class `SummarizedExperiment`.

### Author(s)

Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>

### References

Harrison (2015) Anscombe's 1948 variance stabilizing transformation for the negative binomial distribution is well suited to RNA-seq expression data. doi:10.7490/f1000research.1110757.1

Anscombe (1948) The transformation of Poisson, binomial and negative-binomial data. Biometrika 35(3-4):246-54.

Law et al. (2014) voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol 15:29.

### See Also

`readSE` for reading expression data from file;

`normalizeWithinArrays` and `normalizeBetweenArrays` for normalization of microarray data;

`withinLaneNormalization` and `betweenLaneNormalization` from the EDASeq package for normalization of RNA-seq data;

`cpm`, `estimateDisp`, `voom`, and `varianceStabilizingTransformation` from the DESeq2 package.

### Examples

        #
        # (1) simulating expression data: 100 genes, 12 samples
        #

        # (a) microarray data: intensity measurements
        maSE <- makeExampleData(what="SE", type="ma")

        # (b) RNA-seq data: read counts
        rseqSE <- makeExampleData(what="SE", type="rseq")

        #
        # (2) Normalization
        #

        # (a) microarray ...
        maSE <- normalize(maSE)
        assay(maSE, "raw")[1:5,1:5]
        assay(maSE, "norm")[1:5,1:5]

        # (b) RNA-seq ...
        normSE <- normalize(rseqSE, norm.method = "vst")
        assay(maSE, "raw")[1:5,1:5]
        assay(maSE, "norm")[1:5,1:5]

[Package EnrichmentBrowser version 2.22.2 Index]