extractPCMMDSScales: Generalized Scales-Based Descriptors derived by...

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

View source: R/419-extractPCMMDSScales.R

Description

Generalized Scales-Based Descriptors derived by Multidimensional Scaling

Usage

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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.

Details

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

Value

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

Author(s)

Nan Xiao <https://nanx.me>

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.

See Also

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

Examples

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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)

Rcpi documentation built on June 23, 2017, 4:40 a.m.

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