noisyr_transcript: Run the noisyR pipeline for the transcript approach
In noisyr: Noise Quantification in High Throughput Sequencing Output

Description Usage Arguments Value See Also Examples

Calls the functions to run each of the three steps of the pipeline (similarity calculation, noise quantification, noise removal), with the specified parameters. See the individual function documentation for more details and required arguments. Required steps: calculate_expression_similarity_transcript, calculate_noise_threshold. remove_noise_from_bams. Optional steps: calculate_noise_threshold_method_statistics

noisyr_transcript(
  bams = NULL,
  path.bams = ".",
  genes = NULL,
  path.gtf = list.files(".", pattern = "\\.g[tf]f$"),
  ncores = 1,
  similarity.threshold = 0.25,
  method.chosen = "Boxplot-IQR",
  ...
)

`bams, path.bams`	either a path to the directory where the BAM files are or a vector of paths to each individual file; if a path is specified, it extracts all files that end in .bam; looks in the working directory by default
`genes`	a tibble of the exons extracted from the gtf file; this is meant for speed if the output of `cast_gtf_to_genes` is already generated, or if the user wants to only calculate similarity for a subset of exons
`path.gtf`	the path to the gtf/gff annotation file (only used if genes is not provided); if unspecified, looks for one in the working directory
`ncores`	Number of cores for parallel computation; defaults to sequential computation, but parallelisation is highly encouraged; it is set to detectCores() if higher
`similarity.threshold, method.chosen`	parameters passed on to `calculate_noise_threshold`; they can be single values or vectors; if they are vectors optimal values are computed by calling `calculate_noise_threshold_method_statistics` and minimising the coefficient of variation across samples; all possible values for method.chosen can be viewed by `get_methods_calculate_noise_threshold`; only boxplot based methods are accepted for the transcript approach due to the number of observations and high variance
`...`	arguments to be passed on to individual pipeline steps; see their documentation for more details and required arguments

The denoised BAM files are created, as specified by the destination.files argument of remove_noise_from_bams()

noisyr, noisyr_counts

bams <- rep(system.file("extdata", "ex1.bam", package="Rsamtools", mustWork=TRUE), 2)
genes <- data.frame("id" = 1:2,
                    "gene_id" = c("gene1", "gene2"),
                    "seqid" = c("seq1", "seq2"),
                    "start" = 1,
                    "end" = 1600)
noisyr_transcript(
  bams = bams,
  genes = genes,
  destination.files = paste0(tempdir(), "/", basename(bams), ".noisefiltered.bam")
)