normalize.edaseq {metaseqR} | R Documentation

## Normalization based on the EDASeq package

### Description

This function is a wrapper over EDASeq normalization. It accepts a matrix of gene counts (e.g. produced by importing an externally generated table of counts to the main metaseqr pipeline).

### Usage

        normalize.edaseq(gene.counts, sample.list,
            norm.args = NULL, gene.data = NULL,
            output = c("matrix", "native"))

### Arguments

`gene.counts` | a table where each row represents a gene and each column a sample. Each cell contains the read counts for each gene and sample. Such a table can be produced outside metaseqr and is imported during the basic metaseqr workflow.
`sample.list` | the list containing condition names and the samples under each condition.
`norm.args` | a list of EDASeq normalization parameters. See the result of `get.defaults("normalization",` `"edaseq")` for an example and how you can modify it.
`gene.data` | an optional annotation data frame (such the ones produced by `get.annotation`) which contains the GC content for each gene and from which the gene lengths can be inferred by chromosome coordinates.
`output` | the class of the output object. It can be `"matrix"` (default) for versatility with other tools or `"native"` for the EDASeq native S4 object (SeqExpressionSet). In the latter case it should be handled with suitable EDASeq methods.

### Value

A matrix or a SeqExpressionSet with normalized counts.

### Author(s)

Panagiotis Moulos

### Examples

    require(DESeq)
    data.matrix <- counts(makeExampleCountDataSet())
    sample.list <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
    diagplot.boxplot(data.matrix,sample.list)

    lengths <- round(1000*runif(nrow(data.matrix)))
    starts <- round(1000*runif(nrow(data.matrix)))
    ends <- starts + lengths
    gc=runif(nrow(data.matrix))
    gene.data <- data.frame(
        chromosome=c(rep("chr1",nrow(data.matrix)/2),
            rep("chr2",nrow(data.matrix)/2)),
        start=starts,end=ends,gene_id=rownames(data.matrix),gc_content=gc
    )
    norm.data.matrix <- normalize.edaseq(data.matrix,sample.list,
        gene.data=gene.data)
    diagplot.boxplot(norm.data.matrix,sample.list)

[Package metaseqR version 1.27.13 Index]