totalResults-method: Returns a dataframe with results of the analysis for a...

In vodkatad/roar: Identify differential APA usage from RNA-seq alignments

Description

The last step of a classical Roar analyses: it returns a dataframe containing m/M values, roar values and pvalues.

Usage

      totalResults(rds)
     

Arguments

rds	The RoarDataset or RoarDatasetMultipleAPA with all the analysis steps (countPrePost, computeRoars, computePvals) performed.

Value

The RoarDataset or the RoarDatasetMultipleAPA object given as rds with all the analysis steps performed. If one or more steps hadn't been performed they will be called automatically. The resulting dataframe will have the "gene_id" of the initial annotation as row names (without the trailing "_PRE"/"_POST") and as columns the m/M ratio for the treatment and control conditions, the roar value and the Fisher test pvalue (respectively: mM_treatment, mM_control, roar, pval). If more than one sample has been given for a condition the "pval" column will contain the product of all the comparisons pvalue and there will be other columns containing the pvalues resulting from all the pairwise treatment vs control contrasts, with names "pvalue_X_Y" where X represent the position of the sample in the treatment list of bam files (or GappedAlignment) and Y the position for the control list. When using RoarDatasetMultipleAPA this dataframe will report multiple results for each gene that corresponds to the pairings between every APA associated with that gene in the gtf and the gene's end - rownames in this case will be in the form geneid_apaid. WARNING: this method does not filter in any way the results, therefore there will be negative m/M values/ROAR and also NA - in these cases there aren't enough information to draw a conclusion about the shortening/lengthening of the gene in the given samples and thus the pvalues should not be kept in consideration. Furthermore there isn't any filter on the expression level of the genes. See fpkmResults, standardFilter and pvalueFilter about results filtering possibilities.

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 <- totalResults(rds)
    

vodkatad/roar documentation built on March 30, 2020, 2:56 p.m.