Scales-Based Descriptors derived by Principal Components Analysis

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

Usage

extractScales(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.

See also

See extractDescScales scales descriptors based on 20+ classes of molecular descriptors, and extractProtFP for amino acid property based scales descriptors (protein fingerprint).

Author

Nan Xiao <https://nanx.me>

Examples

x <- readFASTA(system.file("protseq/P00750.fasta", package = "protr"))[[1]]
data(AAindex)
AAidxmat <- t(na.omit(as.matrix(AAindex[, 7:26])))
scales <- extractScales(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