R/417-extractPCMPropScales.R

In nanxstats/Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

#' Generalized AA-Properties Based Scales Descriptors
#'
#' Generalized AA-Properties Based Scales Descriptors
#'
#' This function calculates the generalized amino acid properties based
#' scales descriptors.
#' Users could specify which AAindex properties to select from the
#' AAindex database by specify the numerical or character index of
#' the properties in the AAindex database.
#'
#' @param x A character vector, as the input protein sequence.
#' @param index Integer vector or character vector. Specify which
#'        AAindex properties to select from the AAindex database by
#'        specify the numerical or character index of the properties
#'        in the AAindex database.
#'        Default is \code{NULL}, means selecting all the AA properties
#'        in the AAindex database.
#' @param pc Integer. Use the first pc principal components as the scales.
#'        Must be no greater than the number of AA properties provided.
#' @param lag The lag parameter. Must be less than the amino acids.
#' @param scale Logical. Should we auto-scale the property matrix
#'        before PCA? Default is \code{TRUE}.
#' @param silent Logical. Whether we print the standard deviation,
#'        proportion of variance and the cumulative proportion of
#'        the selected principal components or not.
#'        Default is \code{TRUE}.
#'
#' @return A length \code{lag * p^2} named vector,
#'         \code{p} is the number of scales (principal components) selected.
#'
#' @seealso See \code{\link{extractPCMScales}} for
#' generalized scales-based descriptors.
#'
#' @export extractPCMPropScales
#'
#' @examples
#' x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
#' propscales = extractPCMPropScales(x, index = c(160:165, 258:296), pc = 5, lag = 7, silent = FALSE)
#'

extractPCMPropScales = function (x, index = NULL, pc, lag,
                                 scale = TRUE, silent = TRUE) {

    aaidx = get('AAindex')
    if (!is.null(index)) propmat = t(na.omit(as.matrix(aaidx[index, 7:26])))

    result = extractPCMScales(x = x, propmat = propmat, pc = pc, lag = lag,
                              scale = scale, silent = silent)

    return(result)

}