RoarDataset-constructor: Creates a 'RoarDataset' object
In vodkatad/roar: Identify differential APA usage from RNA-seq alignments

Description Usage Arguments Value See Also Examples

This function creates an RoarDataset object from two lists of of GAlignments and a GRanges containing a suitable annotation of alternative APA sites.

1 2	RoarDataset(treatmentGappedAlign, controlGappedAlign, gtfGRanges)

`treatmentGappedAlign`	A list of `GAlignments` representing alignment of samples for the treatment condition (by convention it is considered the “treated” condition: this simply means that the package will compute roar values (ratios of the m/M) using this condition as the numerator) to be considered.
`controlGappedAlign`	A list of `GAlignments` representing alignment of samples for the control condition to be considered.
`gtfGRanges`	A `GRanges` object with coordinates for the portions of transcripts that has to be considered pertaining to the short (or long) isoform. This `GRanges` object must have a character metadata column called "gene_id" that ends with "_PRE" or "_POST" to address respectively the short and the long isoform. An element in the annotation is considered "PRE" (i.e. common to the short and long isoform of the transcript) if its gene_id ends with "_PRE". If it ends with "_POST" it is considered the portion present only in the long isoform. The prefix of gene_id should be a unique identifier for the gene and each identifier has to be associated with only one "_PRE" and one "_POST", leading to two genomic region associated to each gene_id. The `GRanges` object can also contain a numeric metadata column that represents the lengths of PRE and POST portions on the transcriptome. If this is omitted the lengths on the genome are used instead. Note that right now every gtf entry (or none of them) should have it.

A RoarDataset object ready to be analyzed via the other methods.

RoarDatasetFromFiles

   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)