railMatrix: Identify regions data by a coverage filter and get a count...

View source: R/railMatrix.R

railMatrixR Documentation

Identify regions data by a coverage filter and get a count matrix from BigWig files


Rail (available at http://rail.bio) generates coverage BigWig files. These files are faster to load in R and to process. Rail creates an un-adjusted coverage BigWig file per sample and an adjusted summary coverage BigWig file by chromosome (median or mean). railMatrix reads in the mean (or median) coverage BigWig file and applies a threshold cutoff to identify expressed regions (ERs). Then it goes back to the sample coverage BigWig files and extracts the base level coverage for each sample. Finally it summarizes this information in a matrix with one row per ERs and one column per sample. This function is similar to regionMatrix but is faster thanks to the advantages presented by BigWig files.


  cutoff = NULL,
  maxClusterGap = 300L,
  totalMapped = NULL,
  targetSize = 4e+07,
  file.cores = 1L,
  chrlens = NULL,



A character vector with the names of the chromosomes.


A character vector (or BigWigFileList) with the paths to the summary BigWig files created by Rail. Either mean or median files. These are library size adjusted by default in Rail. The order of the files in this vector must match the order in chrs.


A character vector with the paths to the BigWig files by sample. These files are unadjusted for library size.


The width of the reads used. Either a vector of length 1 or length equal to the number of samples.


The base-pair level cutoff to use. It's behavior is controlled by filter.


This determines the maximum gap between candidate ERs.


A vector with the total number of reads mapped for each sample. The vector should be in the same order as the samples in data. Providing this data adjusts the coverage to reads in targetSize library prior to filtering. See getTotalMapped for calculating this vector.


The target library size to adjust the coverage to. Used only when totalMapped is specified. By default, it adjusts to libraries with 40 million reads, which matches the default used in Rail.


Number of cores used for loading the BigWig files per chr.


The chromosome lengths in base pairs. If it's NULL, the chromosome length is extracted from the BAM files. Otherwise, it should have the same length as chrs.


Arguments passed to other methods and/or advanced arguments. Advanced arguments:


If TRUE basic status updates will be printed along the way. Default: TRUE.


If TRUE basic status updates will be printed along the way when loading data. Default: TRUE.


A BPPARAM object to use for the chr step. Set to SerialParam when file.cores = 1 and SnowParam otherwise.


Chunksize to use. Default: 1000.

Passed to filterData, findRegions and define_cluster.


Given a set of un-filtered coverage data (see fullCoverage), create candidate regions by applying a cutoff on the coverage values, and obtain a count matrix where the number of rows corresponds to the number of candidate regions and the number of columns corresponds to the number of samples. The values are the mean coverage for a given sample for a given region.

This function uses several other derfinder-package functions. Inspect the code if interested.

You should use at most one core per chromosome.


A list with one entry per chromosome. Then per chromosome, a list with two components.


A set of regions based on the coverage filter cutoff as returned by findRegions.


A matrix with the mean coverage by sample for each candidate region.


Leonardo Collado-Torres


## BigWig files are not supported in Windows
if (.Platform$OS.type != "windows") {
    ## Get data

    ## Identify sample files
    sampleFiles <- rawFiles(system.file("extdata", "AMY",
        package =
    ), samplepatt = "bw", fileterm = NULL)
    names(sampleFiles) <- gsub(".bw", "", names(sampleFiles))

    ## Create the mean bigwig file. This file is normally created by Rail
    ## but in this example we'll create it manually.
    fullCov <- fullCoverage(files = sampleFiles, chrs = "chr21")
    meanCov <- Reduce("+", fullCov$chr21) / ncol(fullCov$chr21)
    createBw(list("chr21" = DataFrame("meanChr21" = meanCov)),
        keepGR =

    summaryFile <- "meanChr21.bw"

    ## Get the regions
    regionMat <- railMatrix(
        chrs = "chr21", summaryFiles = summaryFile,
        sampleFiles = sampleFiles, L = 76, cutoff = 5.1,
        maxClusterGap = 3000L

    ## Explore results

lcolladotor/derfinder documentation built on May 4, 2024, 5:38 p.m.