coverage_matrix {recount} | R Documentation

## Given a set of regions for a chromosome, compute the coverage matrix for a given SRA study.

### Description

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.

### Usage

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

### Arguments

`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.

### Details

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()`.

### Value

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

### Author(s)

Leonardo Collado-Torres

### See Also

download_study, findRegions, railMatrix

### Examples

    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))
    }

[Package recount version 1.18.0 Index]