R/extrTAC.R

In wind22zhu/rDNAse: R package for generating various numerical representation schemes of DNA sequences.

#' The Trinucleotide-based Auto Covariance Descriptor
#' 
#' The Trinucleotide-based Auto Covariance Descriptor
#'
#' This function calculates the trinucleotide-based auto covariance Descriptor
#' 
#' @param x the input data, which should be a list or file type.
#' 
#' @param index the physicochemical indices, it should be a list and there are 12
#'              different physicochemical indices (Table 2), which the users can choose.
#'
#' @param nlag an integer larger than or equal to 0 and less than or equal to L-2 (L means the length 
#'             of the shortest DNA sequence in the dataset). It represents the distance between two dinucleotides.
#'
#' @param normaliztion with this option, the final feature vector will be normalized based
#'                  on the total occurrences of all kmers. Therefore, the elements in the feature vectors 
#'                  represent the frequencies of kmers. The default value of this parameter is False.
#' 

#' @param allprop all the 12 physicochemical indices will be
#'                employed to generate the feature vector. Its default value is False.
#'       
#' @param customprops the users can use their own indices to generate the feature vector. It should be a dict, 
#'                    the key is dinucleotide (string), and its corresponding value is a list type.                       
#' 
#' @return A vector 
#' 
#' @keywords extract TAC
#' 
#' @aliases extrTAC
#' 
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>
#' 
#' @export extrTAC
#' 
#' @seealso See \code{\link{extrTCC}} and \code{\link{extrTACC}}
#'          
#' @note if the user defined physicochemical indices have not been normalized, it should be normalized. 
#' 
#' @examples
#'  
#' x = x = 'GACTGAACTGCACTTTGGTTTCATATTATTTGCTC'
#' extrTAC(x)
#'
extrTAC = function (x, index = c('Dnase I', 'Nucleosome'), nlag = 2, normaliztion = FALSE, 
                          customprops = NULL, allprop = FALSE) {
  
  if (!dnacheck(x)) stop('x has character except A, G, C, T' )
  
  Tidx = read.csv(system.file('sysdata/12_trinucleotide_physicochemical_indices.csv', package = 'rDNAse'), header = TRUE)
  tidx = Tidx[, -1]
  row.names(tidx) = Tidx[, 1]
  
  if (allprop) index = Tidx[, 1]
  if (!is.null(customprops)) {
    tidx = rbind(tidx, customprops)
    index = c(as.character(index), rownames(customprops))
  }
  
  n = length(index)
  
  #### normaliztion ####
  Tdict = make_kmer_index(k = 3)
  if (normaliztion) {
    indmean = rowMeans(tidx[index, ])
    indsd = apply(tidx[index, ], 1, sd) 
    
    Pr = data.frame(matrix(ncol = 64, nrow = n))
    for (i in 1:n) Pr[i, ] = (tidx[index[i], ] - indmean[i])/indsd[i]
    names(Pr) = Tdict  
  }
  else Pr = tidx[index, ]
  
  xSplit = strsplit(x, split = "")[[1]]
  xPaste = paste0(xSplit[1:(length(xSplit) - 2)], 
                  xSplit[2:(length(xSplit) - 1)], xSplit[3:(length(xSplit))])
  P = vector('list', n)
  for (i in 1:n) P[[i]] = xPaste
  for (i in 1:n) {
    for (j in Tdict) {
      try(P[[i]][which(P[[i]] == j)] <- Pr[i, j], silent = TRUE)
      
      }
  }
  P = lapply(P, as.numeric)
  
  TAC = vector('list', n)
  N  = length(xPaste)
  
  for (i in 1:n) {
    for (j in 1:nlag) {
      Pbar = lapply(seq_along(P), function(i) P[[i]][1:(N-j)])
      Pbar = sapply(Pbar, sum)/nchar(x) 
      TAC[[i]][j] = (sum((P[[i]][1:(N - j)] - Pbar[i]) * (P[[i]][(1:(N - j)) + j] - Pbar[i])))/(N-j)
    }
  }
  
  TAC = unlist(TAC)
  names(TAC) = as.vector(t(outer(index, paste('.lag', 1:nlag, sep = ''),
                                 paste, sep = '')))
  TAC = round(TAC, 3)
  return(TAC)  
}