R/ModifierML-data.R

In RNAmodR.ML: Detecting patterns of post-transcriptional modifications using machine learning

#' @include RNAmodR.ML.R
#' @include AllGenerics.R
#' @include ModifierML-class.R
NULL

#' @name trainingData
#'
#' @title Assemble training data from aggregate sequence data
#'
#' @description
#' \code{trainingData} subsets the aggregate data of a \code{ModifierML} object
#' to positions as defined by \code{coord}. Positions with an entry in the
#' \code{mod} column are labeled \code{TRUE}.
#'
#' @seealso For more details have a look at
#' \code{\link[RNAmodR:subsetByCoord]{subsetByCoord}}.
#'
#' @param x a \code{ModifierML} object
#' @param coord a \code{GRanges} or a \code{GRangesList} object
#' @param ... See \code{\link[RNAmodR:subsetByCoord]{subsetByCoord}}
#' for more details. \code{type} is hard coded to \code{FALSE} to disregard
#' subsetting by type. In addition, \code{merge} is set to \code{FALSE} by
#' default, but it can be set to \code{TRUE}.
#'
#' @return a \code{CompressedSplitDataFrameList} with aggregate data and an
#' addition label column.
#'
#' @export
#'
#' @examples
#' data("dmod",package = "RNAmodR.ML")
#' setClass("ModMLExample",
#'          contains = c("RNAModifierML"),
#'          prototype = list(mod = c("D"),
#'                           score = "score",
#'                           dataType = c("PileupSequenceData",
#'                                        "CoverageSequenceData"),
#'                           mlModel = character(0)))
#' data("me",package = "RNAmodR.ML")
#' nextUPos <- function(gr){
#'   nextU <- lapply(seq.int(1L,2L),
#'                   function(i){
#'                     subseq <- subseq(RNAmodR::sequences(me)[dmod$Parent], start(dmod)+3L)
#'                     pos <- start(dmod) + 2L +
#'                       vapply(strsplit(as.character(subseq),""),
#'                              function(y){which(y == "U")[i]},integer(1))
#'                     ans <- dmod
#'                     ranges(ans) <- IRanges(start = pos, width = 1L)
#'                     ans
#'                   })
#'   nextU <- do.call(c,nextU)
#'   nextU$mod <- NULL
#'   unique(nextU)
#' }
#' nondmod <- nextUPos(dmod)
#' nondmod <- nondmod[!(nondmod %in% dmod)]
#' coord <- unique(c(dmod,nondmod))
#' coord <- coord[order(as.integer(coord$Parent))]
#' trainingData(me,coord)
NULL

.get_training_data <- function(x, coord, ...){
  labeledData <- labelByCoord(x, coord)
  unlisted_ld <- unlist(labeledData, use.names = FALSE)
  unlisted_ld <-
    unlisted_ld[,!(colnames(unlisted_ld) %in% mainScore(x))]
  labeledData <- relist(unlisted_ld, labeledData)
  args <- list(...)
  if(is.null(args[["merge"]])){
    args[["merge"]] <- FALSE
  }
  do.call(subsetByCoord, c(list(labeledData, coord),args))
}

#' @rdname trainingData
#' @export
setMethod("trainingData",
          signature = c("ModifierML", "GRanges"),
          function(x, coord, ...){
            .get_training_data(x, coord, ...)
          }
)
#' @rdname trainingData
#' @export
setMethod("trainingData",
          signature = c("ModifierML", "GRangesList"),
          function(x, coord, ...){
            .get_training_data(x, coord, ...)
          }
)