metaseqR (version 1.12.2)

stat.noiseq: 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.

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)