stat.noiseq {metaseqR} | R Documentation

## Statistical testing with NOISeq

### Description

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 metaseqR.

### Usage

        stat.noiseq(object, sample.list, contrast.list = NULL,
            stat.args = NULL, gene.data = NULL, log.offset = 1)

### 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) or DGEList (edgeR).
`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 edgeR statistical algorithm parameters. See the result of `get.defaults("statistics",` `"noiseq")` for an example and how you can modify it.
`gene.data` | an optional annotation data frame (such the ones produced by `get.annotation` which contains the GC content for each gene and from which the gene lengths can be inferred by chromosome coordinates.
`log.offset` | a number to be added to each element of data matrix in order to avoid Infinity on log type data transformations.

### Value

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

### 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"
    lengths <- round(1000*runif(nrow(data.matrix)))
    starts <- round(1000*runif(nrow(data.matrix)))
    ends <- starts + lengths
    gc=runif(nrow(data.matrix))
    gene.data <- data.frame(
        chromosome=c(rep("chr1",nrow(data.matrix)/2),
            rep("chr2",nrow(data.matrix)/2)),
        start=starts,end=ends,gene_id=rownames(data.matrix),gc_content=gc
    )
    norm.data.matrix <- normalize.noiseq(data.matrix,sample.list,gene.data)
    p <- stat.noiseq(norm.data.matrix,sample.list,contrast,
        gene.data=gene.data)

[Package metaseqR version 1.20.0 Index]