normalizeNoiseq | R Documentation |
This function is a wrapper over NOISeq normalization. It accepts a matrix of gene counts (e.g. produced by importing an externally generated table of counts to the main metaseqr2 pipeline).
normalizeNoiseq(geneCounts, sampleList,
normArgs = NULL, geneData = NULL, logOffset = 1,
output = c("matrix", "native"))
geneCounts |
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 metaseqr2 and is imported during the basic metaseqr2 workflow. |
sampleList |
the list containing condition names and the samples under each condition. |
normArgs |
a list of NOISeq normalization
parameters. See the result of
|
geneData |
an optional annotation data frame (such
the ones produced by |
logOffset |
an offset to use to avoid infinity in logarithmic data transformations. |
output |
the class of the output object. It can be
|
A matrix with normalized counts.
Panagiotis Moulos
dataMatrix <- metaseqR2:::exampleCountData(2000)
sampleList <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
diagplotBoxplot(dataMatrix,sampleList)
lengths <- round(1000*runif(nrow(dataMatrix)))
starts <- round(1000*runif(nrow(dataMatrix)))
ends <- starts + lengths
gc=runif(nrow(dataMatrix))
geneData <- data.frame(
chromosome=c(rep("chr1",nrow(dataMatrix)/2),
rep("chr2",nrow(dataMatrix)/2)),
start=starts,end=ends,gene_id=rownames(dataMatrix),gc_content=gc,
biotype=rep("protein_coding",nrow(dataMatrix)),
row.names=rownames(dataMatrix)
)
normDataMatrix <- normalizeNoiseq(dataMatrix,sampleList,normArgs=NULL,geneData)
diagplotBoxplot(normDataMatrix,sampleList)
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