stageRTx: Create stageRTx object.
In stageR: stageR: stage-wise analysis of high throughput gene expression data in R

Description Usage Arguments Value References Examples

Constructor function for stageRTxClass. A stageR class is a class used for stage-wise analysis in high throughput settings. In its most basic form, it consists of a vector of p-values for the screening hypothesis, a matrix of p-values for the confirmation hypotheses and a tx2gene object for linking genes to transcripts.

1	stageRTx(pScreen, pConfirmation, pScreenAdjusted = FALSE, tx2gene)

`pScreen`	A vector of screening hypothesis p-values.
`pConfirmation`	A matrix of confirmation hypothesis p-values. The number of rows should be equal to the length of `pScreen`.
`pScreenAdjusted`	logical, indicating whether the supplied p-values for the screening hypothesis have already been adjusted for multiplicity according to the FDR.
`tx2gene`	Only applicable for transcript-level analysis. A `data.frame` with transcript IDs in the first columns and gene IDs in the second column. The rownames from `pConfirmation` must be contained in the transcript IDs from `tx2gene`, and the names from `pScreen` must be contained in the gene IDs.
`...`	Additional arguments.

An instance of an object of the stageRTxClass

Van den Berge K., Soneson C., Robinson M.D., Clement L. (2017). stageR: a general stage-wise method for controlling the gene-level false discovery rate in differential expression and differential transcript usage. Genome Biology 18:151. https://doi.org/10.1186/s13059-017-1277-0

# create a \code{\link{stageRClass}} object
pScreen <- runif(10)
names(pScreen) <- paste0("gene",1:10)
pConfirmation <- matrix(runif(30),nrow=10,ncol=3)
rownames(pConfirmation) <-  paste0("gene",1:10)
stageRObj <- stageR(pScreen=pScreen, pConfirmation=pConfirmation)
pConfirmationTx <- matrix(runif(10),ncol=1)
names(pScreen) <- paste0("gene",rep(1:2,each=5))
stageRObj <- stageRTx(pScreen=pScreen, pConfirmation=pConfirmationTx, tx2gene=data.frame(transcripts=paste0("transcript",1:10),genes=paste0("gene",rep(1:2,each=5))))