nucleR2: Nucleosome positioning package for R

#' Nucleosome positioning package for R
#'
#' Nucleosome positioning from Tiling Arrays and High-Troughput Sequencing
#' Experiments
#'
#' \tabular{ll}{
#'   Package:  \tab nucleR      \cr
#'   Type:     \tab Package     \cr
#'   License:  \tab LGPL (>= 3) \cr
#'   LazyLoad: \tab yes         \cr
#' }
#'
#' This package provides a convenient pipeline to process and analize
#' nucleosome positioning experiments from High-Troughtput Sequencing or Tiling
#' Arrays.
#'
#' Despite it's use is intended to nucleosome experiments, it can be also
#' useful for general ChIP experiments, such as ChIP-on-ChIP or ChIP-Seq.
#'
#' See following example for a brief introduction to the available functions
#'
#' @name nucleR-package
#' @aliases nucleR-package nucleR
#' @docType package
#' @author Oscar Flores Ricard Illa
#'
#' Maintainer: Ricard Illa \email{ricard.illa@@irbbarcelona.org}
#' @keywords package
#' @import methods
#'
#' @examples
#' library(ggplot2)
#' # Load example dataset:
#' # some NGS paired-end reads, mapped with Bowtie and processed with R
#' # it is a GRanges object with the start/end coordinates for each read.
#' reads <- get(data(nucleosome_htseq))
#'
#' # Process the paired end reads, but discard those with length > 200
#' preads_orig <- processReads(reads, type="paired", fragmentLen=200)
#'
#' # Process the reads, but now trim each read to 40bp around the dyad
#' preads_trim <- processReads(reads, type="paired", fragmentLen=200, trim=40)
#'
#' # Calculate the coverage, directly in reads per million (r.p.m.)
#' cover_orig <- coverage.rpm(preads_orig)
#' cover_trim <- coverage.rpm(preads_trim)
#'
#' # Compare both coverages, the dyad is much more clear in trimmed version
#' t1 <- as.vector(cover_orig[[1]])[1:2000]
#' t2 <- as.vector(cover_trim[[1]])[1:2000]
#' t1 <- (t1-min(t1)) / max(t1-min(t1))  # Normalization
#' t2 <- (t2-min(t2)) / max(t2-min(t2))  # Normalization
#'
#' plot_data <- rbind(
#'     data.frame(
#'         x=seq_along(t1),
#'         y=t1,
#'         coverage="original"
#'     ),
#'     data.frame(
#'         x=seq_along(t2),
#'         y=t2,
#'         coverage="trimmed"
#'     )
#' )
#' qplot(x=x, y=y, color=coverage, data=plot_data, geom="line",
#'   xlab="position", ylab="coverage")
#'
#' # Let's try to call nucleosomes from the trimmed version
#' # First of all, let's remove some noise with FFT
#' # Power spectrum will be plotted, look how with a 2%
#' # of the components we capture almost all the signal
#' cover_clean <- filterFFT(cover_trim, pcKeepComp=0.02, showPowerSpec=TRUE)
#'
#' # How clean is it now?
#' plot_data <- rbind(
#'     data.frame(
#'         x=1:4000,
#'         y=as.vector(cover_trim[[1]])[1:4000],
#'         coverage="noisy"
#'     ),
#'     data.frame(
#'         x=1:4000,
#'         y=as.vector(cover_clean[[1]])[1:4000],
#'         coverage="filtered"
#'     )
#' )
#' qplot(x=x, y=y, color=coverage, data=plot_data, geom="line",
#'   xlab="position", ylab="coverage")
#'
#' # And how similar? Let's see the correlation
#' cor(cover_clean[[1]], as.vector(cover_trim[[1]]))
#'
#' # Now it's time to call for peaks, first just as points
#' # See that the score is only a measure of the height of the peak
#' peaks <- peakDetection(cover_clean, threshold="25%", score=TRUE)
#' plotPeaks(peaks[[1]], cover_clean[[1]], threshold="25%")
#'
#' # Do the same as previously, but now we will create the nucleosome calls:
#' peaks <- peakDetection(cover_clean, width=147, threshold="25%", score=TRUE)
#' plotPeaks(peaks, cover_clean[[1]], threshold="25%")
#'
#' #This is all. From here, you can filter, merge or work with the nucleosome
#' #calls using standard IRanges functions and R/Bioconductor manipulation
#'
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