R/MaskedDNAString.EXTS.R

In HenrikBengtsson/aroma.seq: High-Throughput Sequence Analysis using the Aroma Framework

setMethodS3("binTabulate", "MaskedDNAString", function(seq, bx, letters=c("A", "C", "G", "T"), ...) {
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # Validate arguments
  # - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
  # Argument 'bx':
  if (!is.numeric(bx)) {
    stop("Argument 'bx' is not numeric: ", mode(bx))
  }
  if (any(is.infinite(bx))) {
    stop("Argument 'bx' must not contain Inf values.")
  }
  o <- order(bx)
  if (any(diff(o) != 1L)) {
    stop("Argument 'bx' is not ordered.")
  }

  # Argument 'letters':
  letters <- Arguments$getCharacters(letters)
  letters <- unlist(strsplit(letters, split=""), use.names=FALSE)


  # Pull out the actual sequence data
  # NB: Can this be done more efficient?
  # Alt 1: Not sure this is guaranteed to start with base #1. /HB 2012-10-17
  # x <- toString(seq)

  # Alt 2:
  seq <- unmasked(seq)
  # Get the offset of the sequences to be binned.  Should be zero
  # in most (all?) cases.  If non-zero, we'll adjust the binning
  # accordingly.
  offset <- seq@offset
  .stop_if_not(offset >= 0L)

  # Adjust for sequence offset?
  if (offset != 0L) {
    # Adjusting the bins, rather than the sequence offset,
    # should be more efficient since they are fewer and it
    # has only to be once.
    bx <- bx - offset
  }

  # Coerce to a character string.
  x <- toString(seq)

  # Not needed anymore
  rm(seq)

  # Coerce into raw
  x <- charToRaw(x)

  # Tabulate
  counts <- matrix(0L, nrow=length(bx)-1L, ncol=length(letters))
  colnames(counts) <- letters
  for (letter in letters) {
    z <- which(x == charToRaw(letter))
    counts[,letter] <- binCounts(z, bx=bx, ...)
    rm(z)
  }

  # Not needed anymore
  rm(x)

  counts
}) # binTabulate()