computePvals {roar} | R Documentation

## Computes pvalues (Fisher test) on the read counts in this roar analysis

### Description

This is the third step in the Roar analyses: it applies a Fisher test comparing counts falling on the PRE and POST portion in the treatment and control conditions for every gene. If there are multiple samples for a condition every combinations of comparisons between the samples lists are considered.

### Usage

          computePvals(rds)

### Arguments

`rds` | The `RoarDataset` or the `RoarDatasetMultipleAPA` which contains the counts over PRE-POST portions in the two conditions to be compared via pvalues.

### Value

The `RoarDataset` or the `RoarDatasetMultipleAPA` object given as rds with the compute pvalue phase of the analysis done. Pvalues will be held in the RoarDataset object itself in the case of single samples, while in a separate slot otherwise, but end user normally should not analyze those directly but use `totalResults` or `fpkmResults` at the end of the analysis.

### Examples

       library(GenomicAlignments)
       gene_id <- c("A_PRE", "A_POST", "B_PRE", "B_POST")
       features <- GRanges(
          seqnames = Rle(c("chr1", "chr1", "chr2", "chr2")),
          strand = strand(rep("+", length(gene_id))),
          ranges = IRanges(
             start=c(1000, 2000, 3000, 3600),
             width=c(1000, 900, 600, 300)),
          DataFrame(gene_id)
       )
       rd1 <- GAlignments("a", seqnames = Rle("chr1"), pos = as.integer(1000), cigar = "300M", strand = strand("+"))
       rd2 <- GAlignments("a", seqnames = Rle("chr1"), pos = as.integer(2000), cigar = "300M", strand = strand("+"))
       rd3 <- GAlignments("a", seqnames = Rle("chr2"), pos = as.integer(3000), cigar = "300M", strand = strand("+"))
       rds <- RoarDataset(list(c(rd1,rd2)), list(rd3), features)
       rds <- countPrePost(rds, FALSE)
       rds <- computeRoars(rds)
       rds <- computePvals(rds)

[Package roar version 1.18.0 Index]