R/hicrep.R

In hicrep: Measuring the reproducibility of Hi-C data

#' HiCRep pipeline calculates reproducibility of Hi-C intrachromosome data
#'
#' The pipelne is a two-step method. The first step is to smooth the Hi-C 
#' matrix, and the #' second step is to calculate the stratum-adjusted 
#' correlation coefficient (scc). The method also provides the estimation
#' of asymptotic standard deviation of scc.
#'
#' @details
#' \itemize{
#'   \item{Package:    }{hicrep}
#'   \item{Type:    }{Package}
#'   \item{Version:    }{0.99.6}
#'   \item{Date:    }{2017-2-5}
#'   \item{License:    }{GPL-2}
#'   \item{LazyLoad:    }{Yes}
#' }
#'
#' The main functions are \code{\link{prep}}, \code{\link{get.scc}} and
#' \code{\link{htrain}}. The function \code{\link{prep}} will take the 
#' two replicates of \eqn{N*(3+N)} matrix format as input, and return 
#' the vectorized, smoothed or unsmoothed (when smoothing neighborhood
#' size parameter h = 0) Hi-C data, which will subsequently used to 
#' compute stratum-adjusted correlation coefficients (scc). The function
#' \code{\link{get.scc}} computes scc and its asymptotic standard  
#' deviation, and the function \code{\link{htrain}} estimates optimal
#' smoothing neighborhood size from the input matrices.
#' @author
#' Tao Yang
#' Maintainer: Tao Yang <xadmyangt@gmail.com>
#' @references
#' HiCRep: assessing the reproducibility of Hi-C data using a stratum-adjusted
#' correlation coefficient. Tao Yang, Feipeng Zhang, Galip Gurkan Yardimci, 
#' Ross C Hardison, William Stafford Noble, Feng Yue, Qunhua Li. 
#' bioRxiv 101386; doi: https://doi.org/10.1101/101386.
#' @examples
#' data(HiCR1)
#' data(HiCR2)
#'
#' #Estimate the optimial smoothing neighborhood size parameter
#' h_hat <- htrain(HiCR1, HiCR2, 1000000, 5000000, 0:2)
#' h_hat <- 0
#' processed <- prep(HiCR1, HiCR2, 1000000, h_hat, 5000000)
#'
#' scc.out <- get.scc(processed, 1000000, 5000000)
#' scc.out$scc
#' scc.out$std


"_PACKAGE"