R/711-acc.R

In BioMedR: Generating Various Molecular Representations for Chemicals, Proteins, DNAs/RNAs and Their Interactions

#' Auto Cross Covariance (ACC) for Generating Scales-Based Descriptors 
#' of the Same Length
#'
#' Auto Cross Covariance (ACC) for Generating Scales-Based Descriptors 
#' of the Same Length
#' 
#' This function calculates the auto covariance and auto cross covariance 
#' for generating scale-based descriptors of the same length.
#' 
#' @param mat A \code{p * n} matrix. Each row represents one scale 
#' (total \code{p} scales), each column represents one amino acid position 
#' (total \code{n} amino acids).
#' 
#' @param lag The lag parameter. Must be less than the amino acids.
#' 
#' @return A length \code{lag * p^2} named vector, the element names are 
#'         constructed by: the scales index (crossed scales index) and lag index.
#' 
#' @note To know more details about auto cross covariance, see the references.
#' 
#' @keywords acc auto cross covariance
#'
#' @aliases acc
#' 
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>, 
#'         Nan Xiao <\url{http://r2s.name}>
#' 
#' @seealso See \code{\link{extrPCMScales}} for 
#' generalized scales-based descriptors.
#' For more details, see \code{\link{extrPCMDescScales}}
#' and \code{\link{extrPCMPropScales}}.
#' 
#' @export acc
#' 
#' @references
#' Wold, S., Jonsson, J., Sj\"{o}rstr\"{o}m, M., Sandberg, 
#' M., & R\"{a}nnar, S. (1993). 
#' DNA and peptide sequences and chemical processes multivariately modelled 
#' by principal component analysis and partial least-squares projections 
#' to latent structures. 
#' \emph{Analytica chimica acta}, 277(2), 239--253.
#' 
#' Sj\"{o}str\"{o}m, M., R\"{a}nnar, S., & Wieslander, A. (1995). 
#' Polypeptide sequence property relationships in \emph{Escherichia coli} 
#' based on auto cross covariances. 
#' \emph{Chemometrics and intelligent laboratory systems}, 29(2), 295--305.
#' 
#' @examples
#' p = 8    # p is the scales number
#' n = 200  # n is the amino acid number
#' lag = 7  # the lag paramter
#' mat = matrix(rnorm(p * n), nrow = p, ncol = n)
#' acc(mat, lag)
#' 

acc = function (mat, lag) {

    p = nrow(mat)
    n = ncol(mat)

    if (lag > n) stop('lag must be smaller than the amino acids in the sequence')

    # auto covariance: p elements

    acc1 = matrix(0, nrow = p, ncol = lag)
  
    for (j in 1:p) {
        for (i in 1:lag) {
            acc1[j, i] = sum(mat[j, 1:(n - i)] * mat[j, ((1:(n - i)) + i)]) / (n - i)
        }
    }

    acc1 = as.vector(acc1)
    names(acc1) = as.vector(outer(paste0('scl', 1:p), paste0('lag', 1:lag), 
                                  paste, sep = '.'))

    # auto cross covariance: p^2 - p elements

    acc2 = matrix(0, nrow = p^2 - p, ncol = lag)

    idx = cbind(combn(1:p, 2), combn(1:p, 2)[2:1, ])

    for (j in 1:ncol(idx)) {
        for (i in 1:lag) {
            acc2[j, i] = sum(mat[idx[1, j], 1:(n - i)] * mat[idx[2, j], ((1:(n - i)) + i)]) / (n - i)
        }
    }

    acc2 = as.vector(acc2)
    names(acc2) = as.vector(outer(paste0('scl', 
                                         paste(idx[1, ], idx[2, ], sep = '.')), 
                                  paste0('lag', 1:lag), paste, sep = '.'))

    ACC = c(acc1, acc2)

    return(ACC)

}