R/518-extractPCMFAScales.R

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

#' Generalized Scales-Based Descriptors derived by Factor Analysis
#'
#' Generalized Scales-Based Descriptors derived by Factor Analysis
#' 
#' This function calculates the generalized scales-based descriptors 
#' derived by Factor Analysis (FA).
#' Users could provide customized amino acid property matrices.
#' 
#' @param x A character vector, as the input protein sequence.
#' @param propmat A matrix containing the properties for the amino acids. 
#'        Each row represent one amino acid type, each column represents 
#'        one property.
#'        Note that the one-letter row names must be provided for we need 
#'        them to seek the properties for each AA type.
#' @param factors Integer. The number of factors to be fitted.
#'        Must be no greater than the number of AA properties provided.
#' @param scores Type of scores to produce. The default is \code{"regression"}, 
#'        which gives Thompson's scores, \code{"Bartlett"} given 
#'        Bartlett's weighted least-squares scores.
#' @param lag The lag parameter. Must be less than the amino acids number 
#'            in the protein sequence.
#' @param scale Logical. Should we auto-scale the property matrix 
#'        (\code{propmat}) before doing Factor Analysis? Default is \code{TRUE}.
#' @param silent Logical. Whether we print the SS loadings, 
#'        proportion of variance and the cumulative proportion of 
#'        the selected factors or not.
#'        Default is \code{TRUE}.
#'        
#' @return A length \code{lag * p^2} named vector, 
#'         \code{p} is the number of scales (factors) selected.
#' 
#' @keywords extract Factor Analysis extrPCMFAScales PCM
#'
#' @aliases extrPCMFAScales
#' 
#' @author Min-feng Zhu <\email{wind2zhu@@163.com}>, 
#'         Nan Xiao <\url{http://r2s.name}>
#' 
#' @export extrPCMFAScales
#' 
#' @references
#' Atchley, W. R., Zhao, J., Fernandes, A. D., & Druke, T. (2005). 
#' Solving the protein sequence metric problem. 
#' Proceedings of the National Academy of Sciences of the 
#' United States of America, 
#' 102(18), 6395-6400.
#' 
#' @examples
#' x = readFASTA(system.file('protseq/P00750.fasta', package = 'BioMedR'))[[1]]
#' data(AATopo)
#' tprops = AATopo[, c(37:41, 43:47)]  # select a set of topological descriptors
#' fa = extrPCMFAScales(x, propmat = tprops, factors = 5, lag = 7, silent = FALSE)
#' 

extrPCMFAScales = function (x, propmat, factors, scores = 'regression', lag, 
                               scale = TRUE, silent = TRUE) {

    if (checkProt(x) == FALSE) stop('x has unrecognized amino acid type')

    factors = min(factors, ncol(propmat), nrow(propmat))

    if (scale) propmat = scale(propmat)

    prop.fa = factanal(propmat, factors = factors, scores = scores)
    prop.scores = prop.fa$scores

    accmat = matrix(0, factors, nchar(x))
    x.split = strsplit(x, '')[[1]]

    for (i in 1:nchar(x)) {
        accmat[, i] = prop.scores[x.split[i], 1:factors]
    }

    result = acc(accmat, lag)

    if (!silent) {
        cat('Summary of the factor analysis result:\n')
        print(prop.fa)
    }

    return(result)

}