countResults {roar} | R Documentation

## Returns a dataframe with results of the analysis for a `RoarDataset` object or a `RoarDatasetMultipleAPA` object

### Description

The last step of a classical Roar analyses: it returns a dataframe containing m/M values, roar values, pvalues and estimates of expression (number of reads falling over the PRE portions).

### Usage

          countResults(rds)

### Arguments

`rds` | The `RoarDataset` or the `RoarDatasetMultipleAPA` with all the analysis steps (`countPrePost`, `computeRoars`, `computePvals`) performed. If one or more steps hadn't been performed they will be called automatically.

### Value

The resulting dataframe will be identical to that returned by `link{totalResults}` but with two columns added: "treatmentValue" and "controlValue". These columns will contain a number that indicates the level of expression of the relative gene in the treatment (or control) condition. For `RoarDataset` this number represents the counts (averaged across samples when applicable) obtained for the PRE portion of the gene. For `RoarDatasetMultipleAPA` every possible PRE choice will have its corresponding reads counts assigned and also the length of the PRE portion (counting only exonic bases). See the vignette for more details.

### 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)
       dat <- countResults(rds)

[Package roar version 1.18.0 Index]