Generalized Scales-Based Descriptors derived by Principal Components Analysis

Usage

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.

Value

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

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.).

Examples

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)
#> Summary of the first 5 principal components:
#>                             PC1      PC2      PC3      PC4      PC5
#> Standard deviation     13.71695 8.924017 7.698803 6.110576 5.413655
#> Proportion of Variance  0.35434 0.149980 0.111620 0.070320 0.055190
#> Cumulative Proportion   0.35434 0.504320 0.615940 0.686260 0.741450