Generalized Scales-Based Descriptors derived by Multidimensional Scaling

Usage

extractPCMMDSScales(x, propmat, k, lag, scale = TRUE, silent = TRUE)

Arguments

x: A character vector, as the input protein sequence.
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.
k: Integer. The maximum dimension of the space which the data are to be represented in. Must be no greater than the number of AA properties provided.
lag: The lag parameter. Must be less than the amino acids.
scale: Logical. Should we auto-scale the property matrix (propmat) before doing MDS? Default is TRUE.
silent: Logical. Whether we print the k eigenvalues computed during the scaling process or not. Default is TRUE.

Value

A length lag * p^2 named vector, p is the number of scales (dimensionality) selected.

Details

This function calculates the generalized scales-based descriptors derived by Multidimensional Scaling (MDS). Users could provide customized amino acid property matrices.

References

Venkatarajan, M. S., & Braun, W. (2001). New quantitative descriptors of amino acids based on multidimensional scaling of a large number of physical-chemical properties. Molecular modeling annual, 7(12), 445–453.

Examples

x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
data(AATopo)
tprops = AATopo[, c(37:41, 43:47)]  # select a set of topological descriptors
mds = extractPCMMDSScales(x, propmat = tprops, k = 5, lag = 7, silent = FALSE)
#> Eigenvalues computed during the scaling process:
#>  [1]  1.266223e+02  5.846270e+01  4.058692e+00  6.994430e-01  1.529469e-01
#>  [6]  3.434787e-03  4.284842e-04  4.918500e-05  3.626185e-06  3.042620e-10
#> [11]  2.295818e-15  1.850329e-15  1.455955e-15  1.208436e-15  6.929084e-16
#> [16]  5.639676e-16 -9.663800e-16 -1.207752e-15 -2.504939e-15 -3.313120e-15