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\name{plotCNEWidth}
\alias{plotCNEWidth}
\title{
Plot the CNE widths distribution
}
\description{
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.
}
\usage{
plotCNEWidth(x, ...)
}
\arguments{
\item{x}{
\code{GRangePairs} object: a pair of CNEs.
}
\item{\dots}{
Additional points passed to \code{plot} function.
}
}
\details{
The power-law distribution is associated with heavy tailed distribution.
A reverse cumulative density distribution plot will be generated with optimal
lower bound \emph{xmin}, scaling parameter\emph{alpha} for power-law fit.
}
\value{
An invisible list of fitted model is returned.
}
\references{
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.
}
\author{
Ge Tan
}
\note{
The power-law distribution implementation is based on the \pkg{poweRlaw} package.
}
\examples{
dbName <- file.path(system.file("extdata", package="CNEr"),
"danRer10CNE.sqlite")
cneGRangePairs <- readCNERangesFromSQLite(dbName=dbName,
tableName="danRer10_hg38_45_50")
plotCNEWidth(cneGRangePairs)
}
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