extractPCMMDSScales: Generalized Scales-Based Descriptors derived by...
In Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

Description Usage Arguments Details Value Author(s) References See Also Examples

Generalized Scales-Based Descriptors derived by Multidimensional Scaling

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

`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`.

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

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

Nan Xiao <https://nanx.me>

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.

See extractPCMScales for generalized scales-based descriptors derived by Principal Components Analysis.

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)