statNoiseq | R Documentation |
This function is a wrapper over NOISeq statistical testing. It accepts a matrix of normalized gene counts or an S4 object specific to each normalization algorithm supported by metaseqR2.
statNoiseq(object, sampleList, contrastList = NULL,
statArgs = NULL, geneData = NULL, logOffset = 1)
object |
a matrix or an object specific to each normalization algorithm supported by metaseqR2, containing normalized counts. See also Details. |
sampleList |
the list containing condition names and the samples under each condition. |
contrastList |
vector of contrasts as defined in the
main help page of |
statArgs |
a list of edgeR statistical algorithm
parameters. See the result of
|
geneData |
an optional annotation data frame (such
the ones produced by |
logOffset |
a number to be added to each element of data matrix in order to avoid Infinity on log type data transformations. |
Regarding object
, apart from matrix
(also
for NOISeq), the object can be a SeqExpressionSet
(EDASeq), CountDataSet
(DESeq), DGEList
(edgeR), DESeqDataSet
(DESeq2), SeqCountSet
(DSS) or ABSDataSet
(ABSSeq).
Regarding contrastList
it can also be a named
structured list of contrasts as returned by the internal
function metaseqR2:::makeContrastList
.
A named list of NOISeq q-values, whose names are the names of the contrasts.
Panagiotis Moulos
dataMatrix <- metaseqR2:::exampleCountData(1000)
sampleList <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
contrast <- "A_vs_B"
lengths <- round(1000*runif(nrow(dataMatrix)))
starts <- round(1000*runif(nrow(dataMatrix)))
ends <- starts + lengths
gc=runif(nrow(dataMatrix))
biotype=rep("protein_coding",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=biotype
)
normArgs <- metaseqR2:::getDefaults("normalization","noiseq")
normDataMatrix <- normalizeNoiseq(dataMatrix,sampleList,normArgs,
geneData)
p <- statNoiseq(normDataMatrix,sampleList,contrast,
geneData=geneData)
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