totalResults {roar} | R Documentation

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

### 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)

[Package roar version 1.20.0 Index]