Generalized Scales-Based Descriptors derived by Principal Components Analysis

Description

Generalized Scales-Based Descriptors derived by Principal Components Analysis

Usage

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extractPCMScales(x, propmat, pc, 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.

pc

Integer. Use the first pc principal components as the scales. 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 PCA? Default is TRUE.

silent

Logical. Whether we print the standard deviation, proportion of variance and the cumulative proportion of the selected principal components or not. Default is TRUE.

Details

This function calculates the generalized scales-based descriptors derived by Principal Components Analysis (PCA). Users could provide customized amino acid property matrices. This function implements the core computation procedure needed for the generalized scales-based descriptors derived by AA-Properties (AAindex) and generalized scales-based descriptors derived by 20+ classes of 2D and 3D molecular descriptors (Topological, WHIM, VHSE, etc.).

Value

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

Author(s)

Nan Xiao <http://nanx.me>

See Also

See extractPCMDescScales for generalized AA property based scales descriptors, and extractPCMPropScales for (19 classes) AA descriptor based scales descriptors.

Examples

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x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
data(AAindex)
AAidxmat = t(na.omit(as.matrix(AAindex[, 7:26])))
scales = extractPCMScales(x, propmat = AAidxmat, pc = 5, lag = 7, silent = FALSE)

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