pcm-06-extractBLOSUM.R

#' BLOSUM and PAM Matrix-Derived Descriptors
#'
#' This function calculates BLOSUM matrix-derived descriptors.
#' For users' convenience, \code{protr} provides the BLOSUM45, BLOSUM50,
#' BLOSUM62, BLOSUM80, BLOSUM100, PAM30, PAM40, PAM70, PAM120, and PAM250
#' matrices for the 20 amino acids to select from.
#'
#' @param x A character vector, as the input protein sequence.
#' @param submat Substitution matrix for the 20 amino acids. Should be one of
#' \code{AABLOSUM45}, \code{AABLOSUM50}, \code{AABLOSUM62},
#' \code{AABLOSUM80}, \code{AABLOSUM100}, \code{AAPAM30},
#' \code{AAPAM40}, \code{AAPAM70}, \code{AAPAM120}, or \code{AAPAM250}.
#' Default is \code{"AABLOSUM62"}.
#' @param k Integer. The number of selected scales (i.e. the first
#' \code{k} scales) derived by the substitution matrix.
#' This can be selected according to the printed relative importance values.
#' @param lag The lag parameter. Must be less than the amino acids.
#' @param scale Logical. Should we auto-scale the substitution matrix
#' (\code{submat}) before doing eigen decomposition? Default is \code{TRUE}.
#' @param silent Logical. Whether we print the relative importance of
#' each scales (diagnal value of the eigen decomposition result matrix B)
#' or not. Default is \code{TRUE}.
#' @return  A length \code{lag * p^2} named vector, \code{p} is the number
#' of scales selected.
#'
#' @author Nan Xiao <\url{https://nanx.me}>
#'
#' @export extractBLOSUM
#'
#' @references
#' Georgiev, A. G. (2009).
#' Interpretable numerical descriptors of amino acid space.
#' Journal of Computational Biology, 16(5), 703--723.
#'
#' @examples
#' x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
#' blosum <- extractBLOSUM(x, submat = "AABLOSUM62", k = 5, lag = 7, scale = TRUE, silent = FALSE)
extractBLOSUM <- function(
  x, submat = "AABLOSUM62", k, lag, scale = TRUE, silent = TRUE) {
  if (protcheck(x) == FALSE) {
    stop("x has unrecognized amino acid type")
  }

  k <- min(k, 20)

  submat <- get(submat)
  if (scale) submat <- scale(submat)

  eig <- eigen(submat)
  A <- eig$vectors
  B <- eig$values
  rownames(A) <- rownames(submat)
  # the equation: submat == A %*% diag(B) %*% t(A)

  accmat <- matrix(0, k, nchar(x))
  x.split <- strsplit(x, "")[[1]]

  for (i in 1:nchar(x)) accmat[, i] <- A[x.split[i], 1:k]

  res <- acc(accmat, lag)

  if (!silent) {
    cat("Relative importance of all the possible 20 scales:", "\n")
    print(B)
  }

  res
}