expressed_regions {recount} | R Documentation

## Identify expressed regions from the mean coverage for a given SRA project

### Description

This function uses the pre-computed mean coverage for a given SRA project to identify the expressed regions (ERs) for a given chromosome. It returns a GRanges-class object with the expressed regions as defined by findRegions.

### Usage

    expressed_regions(
      project,
      chr,
      cutoff,
      outdir = NULL,
      maxClusterGap = 300L,
      chrlen = NULL,
      verbose = TRUE,
      ...
    )

### Arguments

`project` | A character vector with one SRA study id.
`chr` | A character vector with the name of the chromosome.
`cutoff` | The base-pair level cutoff to use.
`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.
`maxClusterGap` | This determines the maximum gap between candidate ERs.
`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.
`...` | Additional arguments passed to download_study when `outdir` is specified but the required files are missing.

### Value

A GRanges-class object as created by findRegions.

### Author(s)

Leonardo Collado-Torres

### See Also

download_study, findRegions, railMatrix

### Examples

    ## Define expressed regions for study SRP009615, chrY
    if (.Platform$OS.type != "windows") {
        ## Reading BigWig files is not supported by rtracklayer on Windows
        regions <- expressed_regions("SRP009615", "chrY",
            cutoff = 5L,
            maxClusterGap = 3000L
        )
    }
    ## Not run:
    ## Define the regions for multiple chrs
    regs <- sapply(chrs, expressed_regions, project = "SRP009615", cutoff = 5L)

    ## You can then combine them into a single GRanges object if you want to
    library("GenomicRanges")
    single <- unlist(GRangesList(regs))

    ## End(Not run)

[Package recount version 1.20.0 Index]