stat.nbpseq {metaseqR} | R Documentation

## Statistical testing with NBPSeq

### Description

This function is a wrapper over NBPSeq statistical testing. It accepts a matrix of normalized gene counts or an S4 object specific to each normalization algorithm supported by metaseqR.

### Usage

        stat.nbpseq(object, sample.list, contrast.list = NULL,
            stat.args = NULL, libsize.list = NULL)

### Arguments

`object` | a matrix or an object specific to each normalization algorithm supported by metaseqR, containing normalized counts. Apart from matrix (also for NOISeq), the object can be a SeqExpressionSet (EDASeq), CountDataSet (DESeq), DGEList (edgeR) or list (NBPSeq).
`sample.list` | the list containing condition names and the samples under each condition.
`contrast.list` | a named structured list of contrasts as returned by `make.contrast.list` or just the vector of contrasts as defined in the main help page of `metaseqr`.
`stat.args` | a list of NBPSeq statistical algorithm parameters. See the result of `get.defaults("statistics",` `"nbpseq")` for an example and how you can modify it. It is not required when the input object is already a list from NBPSeq normalization as the dispersions are already estimated.
`libsize.list` | an optional named list where names represent samples (MUST be the same as the samples `in sample.list`) and members are the library sizes (the sequencing depth) for each sample. If not provided, the default is the column sums of the `gene.counts` matrix.

### Value

A named list of p-values, whose names are the names of the contrasts.

### Note

There is currently a problem with the NBPSeq package and the workflow that is specific to the NBPSeq package. The problem has to do with function exporting as there are certain functions which are not recognized from the package internally. For this reason and until it is fixed, only the Smyth workflow will be available with the NBPSeq package.

### Author(s)

Panagiotis Moulos

### Examples

    require(DESeq)
    data.matrix <- counts(makeExampleCountDataSet())
    sample.list <- list(A=c("A1","A2"),B=c("B1","B2","B3"))
    contrast <- "A_vs_B"
    norm.data.matrix <- normalize.nbpseq(data.matrix,sample.list)
    p <- stat.nbpseq(norm.data.matrix,sample.list,contrast)

[Package metaseqR version 1.20.0 Index]