Generalized Scales-Based Descriptors derived by Factor Analysis

Usage

extractPCMFAScales(
  x,
  propmat,
  factors,
  scores = "regression",
  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.
factors: Integer. The number of factors to be fitted. Must be no greater than the number of AA properties provided.
scores: Type of scores to produce. The default is "regression", which gives Thompson's scores, "Bartlett" given Bartlett's weighted least-squares scores.
lag: The lag parameter. Must be less than the amino acids number in the protein sequence.
scale: Logical. Should we auto-scale the property matrix (propmat) before doing Factor Analysis? Default is TRUE.
silent: Logical. Whether we print the SS loadings, proportion of variance and the cumulative proportion of the selected factors or not. Default is TRUE.

Value

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

Details

This function calculates the generalized scales-based descriptors derived by Factor Analysis (FA). Users could provide customized amino acid property matrices.

References

Atchley, W. R., Zhao, J., Fernandes, A. D., & Druke, T. (2005). Solving the protein sequence metric problem. Proceedings of the National Academy of Sciences of the United States of America, 102(18), 6395-6400.

Examples

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
fa = extractPCMFAScales(x, propmat = tprops, factors = 5, lag = 7, silent = FALSE)
#> Summary of the factor analysis result:
#> 
#> Call:
#> factanal(x = propmat, factors = factors, scores = scores)
#> 
#> Uniquenesses:
#> WhetZ Whetm Whetv Whete Whetp JhetZ Jhetm Jhetv Jhete Jhetp 
#> 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 0.005 
#> 
#> Loadings:
#>       Factor1 Factor2 Factor3 Factor4 Factor5
#> WhetZ  0.982  -0.166                         
#> Whetm  0.982  -0.166                         
#> Whetv  0.989  -0.133                         
#> Whete  0.982  -0.159                         
#> Whetp  0.989  -0.131                         
#> JhetZ -0.399   0.881   0.162   0.192         
#> Jhetm -0.399   0.881   0.162   0.192         
#> Jhetv          0.985          -0.131         
#> Jhete -0.378   0.872   0.300                 
#> Jhetp          0.970  -0.210                 
#> 
#>                Factor1 Factor2 Factor3 Factor4 Factor5
#> SS loadings      5.313   4.340   0.212   0.107   0.003
#> Proportion Var   0.531   0.434   0.021   0.011   0.000
#> Cumulative Var   0.531   0.965   0.986   0.997   0.997
#> 
#> Test of the hypothesis that 5 factors are sufficient.
#> The chi square statistic is 472.2 on 5 degrees of freedom.
#> The p-value is 7.96e-100