computePvalues | R Documentation |
This function computes the P-values based on the fitted negative binomial model being an outlier for the given sample gene combination. It computes two matrices with the same dimension as the count matrix (samples x genes), which contain the corresponding P-values and adjusted P-values of every count. The adjusted P-values are computed across all genes per sample.
computePvalues(object, ...)
## S4 method for signature 'OutriderDataSet'
computePvalues(
object,
alternative = c("two.sided", "greater", "less"),
method = "BY",
subsets = NULL,
BPPARAM = bpparam()
)
object |
An OutriderDataSet |
... |
additional params, currently not used. |
alternative |
Can be one of "two.sided", "greater" or "less" to specify the alternative hypothesis used to calculate the P-values, defaults to "two.sided" |
method |
Method used for multiple testing |
subsets |
A named list of named lists specifying any number of gene subsets (can differ per sample). For each subset, FDR correction will be limited to genes in the subset, and the FDR corrected pvalues stored as an assay in the ods object in addition to the transcriptome-wide FDR corrected pvalues. See the examples for how to use this argument. |
BPPARAM |
Can be used to parallelize the computation, defaults to bpparam() |
An OutriderDataSet object with computed nominal and adjusted P-values
p.adjust
ods <- makeExampleOutriderDataSet()
ods <- estimateSizeFactors(ods)
ods <- fit(ods)
ods <- computePvalues(ods)
assays(ods)[['pValue']][1:10,1:10]
# example of restricting FDR correction to subsets of genes of interest
genesOfInterest <- list("sample_1"=sample(rownames(ods), 3),
"sample_2"=sample(rownames(ods), 8),
"sample_6"=sample(rownames(ods), 5))
ods <- computePvalues(ods, subsets=list("exampleSubset"=genesOfInterest))
padj(ods, subsetName="exampleSubset")[1:10,1:10]
ods <- computePvalues(ods,
subsets=list("anotherExampleSubset"=rownames(ods)[1:5]))
padj(ods, subsetName="anotherExampleSubset")[1:10,1:10]
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