SingleCloseByRegion: Extracts clusters of RNA editing sites located closely in a...

In TransBioInfoLab/rnaEditr: Statistical analysis of RNA editing sites and hyper-editing regions

Extracts clusters of RNA editing sites located closely in a single genomic region.

Description

Extracts clusters of RNA editing sites located closely in an input genomic region.

Usage

SingleCloseByRegion(region_df, rnaEditMatrix, maxGap = 50, minSites = 3)

Arguments

region_df	A data frame with the input genomic region. Please make sure columns seqnames, start, and end are included in the data frame.
rnaEditMatrix	A matrix (or data frame) of RNA editing level values for individual sites, with row names as site IDs in the form of "chrAA:XXXXXXXX", and column names as sample IDs. Please make sure to follow the format of example dataset (data(rnaedit_df)).
maxGap	An integer, genomic locations within maxGap from each other are placed into the same cluster. Defaults to 50.
minSites	An integer, minimum number of edited sites for a cluster to be selected for output. Defaults to 3.

Details

The algorithm of this function is based on the clusterMaker function in the bumphunter R package. Each cluster is essentially a group of sites such that two consecutive sites in the cluster are separated by less than maxGap.

Value

A GRanges object containing genomic locations of RNA editing sites located closely within the single input pre-defined genomic region.

Examples

  data(rnaedit_df)
  
  exm_region <- data.frame(
    seqnames = "chr1",
    start =  28691093,
    end = 28826881, 
    stringsAsFactors = FALSE
  )
  
  SingleCloseByRegion(
    region_df = exm_region,
    rnaEditMatrix = rnaedit_df,
    maxGap = 50,
    minSites = 3
  )  
  

TransBioInfoLab/rnaEditr documentation built on Nov. 29, 2022, 3:31 p.m.