This function wraps commonly used functionality from limma for microarray normalization and from EDASeq for RNA-seq normalization.
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An object of class
Determines how the expression data should be normalized.
For available microarray normalization methods see the man page of the limma
Expression data type. Use
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
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.
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
function is chosen as 0.5 / phi as previously suggested (Harrison, 2015).
An object of class
Ludwig Geistlinger <Ludwig.Geistlinger@sph.cuny.edu>
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.
readSE for reading expression data from file;
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# # (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]
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