normalize.edaseq {metaseqR} | R Documentation |
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).
normalize.edaseq(gene.counts, sample.list, norm.args = NULL, gene.data = NULL, output = c("matrix", "native"))
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
|
gene.data |
an optional annotation data frame (such
the ones produced by |
output |
the class of the output object. It can be
|
A matrix or a SeqExpressionSet with normalized counts.
Panagiotis Moulos
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)