coverage_matrix: Given a set of regions for a chromosome, compute the coverage...
In recount: Explore and download data from the recount project

Description Usage Arguments Details Value Author(s) See Also Examples

Given a set of genomic regions as created by expressed_regions, this function computes the coverage matrix for a library size of 40 million 100 bp reads for a given SRA study.

coverage_matrix(
  project,
  chr,
  regions,
  chunksize = 1000,
  bpparam = NULL,
  outdir = NULL,
  chrlen = NULL,
  verbose = TRUE,
  verboseLoad = verbose,
  scale = TRUE,
  round = FALSE,
  ...
)

`project`	A character vector with one SRA study id.
`chr`	A character vector with the name of the chromosome.
`regions`	A GRanges-class object with regions for `chr` for which to calculate the coverage matrix.
`chunksize`	A single integer vector defining the chunksize to use for computing the coverage matrix. Regions will be split into different chunks which can be useful when using a parallel instance as defined by `bpparam`.
`bpparam`	A BiocParallelParam-class instance which will be used to calculate the coverage matrix in parallel. By default, SerialParam-class will be used.
`outdir`	The destination directory for the downloaded file(s) that were previously downloaded with download_study. If the files are missing, but `outdir` is specified, they will get downloaded first. By default `outdir` is set to `NULL` which will use the data from the web. We only recommend downloading the full data if you will use it several times.
`chrlen`	The chromosome length in base pairs. If it's `NULL`, the chromosome length is extracted from the Rail-RNA runs GitHub repository. Alternatively check the `SciServer` section on the vignette to see how to access all the recount data via a R Jupyter Notebook.
`verbose`	If `TRUE` basic status updates will be printed along the way.
`verboseLoad`	If `TRUE` basic status updates for loading the data will be printed.
`scale`	If `TRUE`, the coverage counts will be scaled to read counts based on a library size of 40 million reads. Set `scale` to `FALSE` if you want the raw coverage counts. The scaling method is by AUC, as in the default option of scale_counts.
`round`	If `TRUE`, the counts are rounded to integers. Set to `TRUE` if you want to match the defaults of scale_counts.
`...`	Additional arguments passed to download_study when `outdir` is specified but the required files are missing.

When using outdir = NULL the information will be accessed from the web on the fly. If you encounter internet access problems, it might be best to first download the BigWig files using download_study. This might be the best option if you are accessing all chromosomes for a given project and/or are thinking of using different sets of regions (for example, from different cutoffs applied to expressed_regions). Alternatively check the SciServer section on the vignette to see how to access all the recount data via a R Jupyter Notebook.

If you have bwtool installed, you can use https://github.com/LieberInstitute/recount.bwtool for faster results. Note that you will need to run scale_counts after running coverage_matrix_bwtool().

A RangedSummarizedExperiment-class object with the counts stored in the assays slot.

Leonardo Collado-Torres

download_study, findRegions, railMatrix

if (.Platform$OS.type != "windows") {
    ## Reading BigWig files is not supported by rtracklayer on Windows
    ## Define expressed regions for study DRP002835, chrY
    regions <- expressed_regions("DRP002835", "chrY",
        cutoff = 5L,
        maxClusterGap = 3000L
    )

    ## Now calculate the coverage matrix for this study
    rse <- coverage_matrix("DRP002835", "chrY", regions)

    ## One row per region
    identical(length(regions), nrow(rse))
}