standardFilter {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 (a measure recalling FPKM). Only the genes with an expression estimate bigger than a given cutoff will be considered.

### Usage

          standardFilter(rds, fpkmCutoff)

### 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.
`fpkmCutoff` | The cutoff that will be used to determine if a gene is expressed or not.

### Value

For `RoarDataset` and `RoarDatasetMultipleAPA`:

The resulting dataframe will be identical to that returned by `fpkmResults` but it will contains rows relative only with genes with an expression estimate (treatment or controlValue) bigger than the given fpkmCutoff in both the conditions and with sensitive m/M and roar values (it removes negative or NA m/M values/roar - these values arise when there aren't enough information to draw a conclusion about the shortening/lengthening of the gene).

### 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 <- standardFilter(rds, 1)

[Package roar version 1.18.0 Index]