SingleCloseByRegion: Extracts clusters of RNA editing sites located closely in a...
In rnaEditr: Statistical analysis of RNA editing sites and hyper-editing regions

Description Usage Arguments Details Value Examples

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

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

`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.

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.

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

  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
  )