plotCNEWidth: Plot the CNE widths distribution
In CNEr: CNE Detection and Visualization

Description Usage Arguments Details Value Note Author(s) References Examples

CNE widths can follow heavy tailed distribution that are associated with power-laws. This function plots the reverse cumulative density distribution of CNE widths, and fits a discrete power-law distribution. Goodness of fit can also be evaluated.

1	plotCNEWidth(x, ...)

`x`	`GRangePairs` object: a pair of CNEs.
`...`	Additional points passed to `plot` function.

The power-law distribution is associated with heavy tailed distribution.

A reverse cumulative density distribution plot will be generated with optimal lower bound xmin, scaling parameteralpha for power-law fit.

An invisible list of fitted model is returned.

The power-law distribution implementation is based on the poweRlaw package.

Ge Tan

Salerno, W., Havlak, P., and Miller, J. (2006). Scale-invariant structure of strongly conserved sequence in genomic intersections and alignments. Proc. Natl. Acad. Sci. U.S.A. 103, 13121-13125.

  dbName <- file.path(system.file("extdata", package="CNEr"),
                      "danRer10CNE.sqlite")
  cneGRangePairs <- readCNERangesFromSQLite(dbName=dbName, 
                                            tableName="danRer10_hg38_45_50")
  plotCNEWidth(cneGRangePairs)