Normalization of microarray and RNA-seq expression data
This function wraps commonly used functionality from limma for microarray normalization and from EDASeq for RNA-seq normalization.
An object of
Determines how the expression data should be normalized.
For available microarray normalization methods see the man page of the
Is only taken into account if data.type='rseq'.
Determine whether GC content normalization should be carried out (as
implemented in the EDASeq function
Expression data type. Use 'ma' for microarray and 'rseq' for RNA-seq data. If NA, data.type is automatically guessed. If the expression values in 'eset' are decimal numbers they are assumed to be microarray intensities. Whole numbers are assumed to be RNA-seq read counts. Defaults to NA.
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 these standard normalizations, the user should always refer to more specific functions/packages.
Microarray data is expected to be single-channel.
For two-color arrays, it is expected here that normalization within arrays
has been already carried out, e.g. using
RNA-seq data is expected to be raw read counts. Please note that normalization for downstream DE analysis, e.g. with edgeR and DESeq, 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 DESeq for details.
An object of
For RNA-seq data, an object of
to conform with downstream DE analysis.
Ludwig Geistlinger <Ludwig.Geistlinger@bio.ifi.lmu.de>
read.eset describes prerequisites for the expression data;
normalizeBetweenArrays for normalization of microarray data;
betweenLaneNormalization for normalization of RNA-seq data.
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# # (1) simulating expression data: 100 genes, 12 samples # # (a) microarray data: intensity measurements ma.eset <- make.example.data(what="eset", type="ma") # (b) RNA-seq data: read counts rseq.eset <- make.example.data(what="eset", type="rseq") # # (2) Normalization # # (a) microarray ... norm.eset <- normalize(ma.eset) # (b) RNA-seq ... norm.eset <- normalize(rseq.eset) # ... normalize also for GC content gc.content <- rnorm(100, 0.5, sd=0.1) fData(rseq.eset)$gc <- gc.content norm.eset <- normalize(rseq.eset, within=TRUE)
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