expressed_regions: Identify expressed regions from the mean coverage for a given...
In recount: Explore and download data from the recount project

Description Usage Arguments Value Author(s) See Also Examples

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.

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

`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.

A GRanges-class object as created by findRegions.

Leonardo Collado-Torres

download_study, findRegions, railMatrix

## 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)