\docType{methods}
\name{segTable}
\alias{segTable}
\alias{segTable,DataFrame-method}
\alias{segTable,Rle-method}
\title{Convert Rle objects to tables of segments}
\arguments{
  \item{object}{Rle or list/DataFrame of Rle vectors}

  \item{locs}{RangedData with rows corresponding to rows of
  df}

  \item{chr.ind}{matrix, like from chrIndices method}

  \item{start}{integer, vector of feature start positions}

  \item{end}{integer, vector of feature end positions}

  \item{stack}{logical, rbind list of segment tables for
  each sample and add "Sample" column?}
}
\value{
  one or a list of data.frames with columns chrom,
  loc.start, loc.end, num.mark, seg.mean
}
\description{
  Like the inverse of segs2Rle and segs2RleDataFrame. Takes
  a Rle or a DataFrame with Rle columns and the locData
  RangedData both from a GenoSet object and make a list of
  data.frames each like the result of CBS's segment.  Note
  the loc.start and loc.stop will correspond exactly to
  probe locations in locData and the input to
  segs2RleDataFrame are not necessarily so. For a
  DataFrame, the argument \code{stack} combines all of the
  individual data.frames into one large data.frame and adds
  a "Sample" column of sample ids.
}
\details{
  For a Rle, the user can provide \code{locs} or
  \code{chr.ind}, \code{start} and \code{stop}.  The latter
  is surprisingly much faster and this is used in the
  DataFrame version.
}
\examples{
data(genoset)
  seg.list = runCBS( lrr(baf.ds), locData(baf.ds), return.segs=TRUE )
  df = segs2RleDataFrame( seg.list, locData(baf.ds) )  # Loop segs2Rle on list of data.frames in seg.list
  assayDataElement( baf.ds, "lrr.segs" ) = df
  segTable( df, locData(baf.ds) )
  segTable( assayDataElement(baf.ds,"lrr.segs"), locData(baf.ds) )
  segTable( assayDataElement(baf.ds,"lrr.segs")[,1], locData(baf.ds), sampleNames(baf.ds)[1] )
}
\author{
  Peter M. Haverty
}
\seealso{
  Other "segmented data": \code{\link{runCBS}},
  \code{\link{segs2RangedData}}, \code{\link{segs2Rle}},
  \code{\link{segs2RleDataFrame}}
}