tileCount2: Perform overlap queries between reads and genome by sliding...
In NADfinder: Call wide peaks for sequencing data

Description Usage Arguments Value Author(s) Examples

View source: R/tileCount2.R View source: R/tileCount2.R

Perform overlap queries between reads and genome by sliding windows Count reads over sliding windows.

if (interactive()) fls <- list.files(system.file("extdata", package="NADfinder"), recursive=FALSE, pattern="*bam$", full=TRUE) names(fls) <- basename(fls)

se <- tileCount2(reads = fls, windowSize=50000, step=10000)

tileCount2(
  reads,
  fragment.length = 100,
  windowSize = 50000,
  restrict = paste0("chr", c(1:19, "X", "Y")),
  step = 1000,
  filter = 0,
  pe = "both"
)

tileCount2(
  reads,
  fragment.length = 100,
  windowSize = 50000,
  restrict = paste0("chr", c(1:19, "X", "Y")),
  step = 1000,
  filter = 0,
  pe = "both"
)

`reads`	An object that represents the names and path of the bam files to be counted. If reads are more than 1 bam files, it should be a vector of character with full path. This function now works for paired end reads
`fragment.length`	integer(1). An integer scalar or a list of two integer scalars/vectors, containing the average length(s) of the sequenced fragments in each libary.
`windowSize`	numeric(1) or integer(1). Size of the windows.
`restrict`	restrict to a set of chromosomes, default to mouse chromosomes.
`step`	numeric(1) or integer(1). Step of generating silding windows.
`filter`	default to 0 without filtering. An integer scalar for the minimum count sum across libraries for each window
`pe`	a character string indicating whether paired-end data is present; set to "none", "both", "first" or "second"

A RangedSummarizedExperiment object with chromosome-level depth The assays slot holds the counts, rowRanges holds the annotation from the sliding widows of genome. metadata contains lib.size.chrom for holding chromosome-level sequence depth

Jun Yu,Herv<c3><a9> Pag<c3><a8>s and Julie Zhu

if (interactive())
{
    fls <- list.files(system.file("extdata", package="NADfinder"),
    recursive=FALSE, pattern="*bam$", full=TRUE)
    names(fls) <- basename(fls)
   
    se <- tileCount2(reads = fls,
                    windowSize=50000, step=10000)
}