Principal Component Analysis

Performs principal components analysis (PCA) on biomolecular structure data.

pca(...)

Arguments

...	arguments passed to the methods pca.xyz, pca.pdbs, etc. Typically this includes either a numeric matrix of Cartesian coordinates with a row per structure/frame (function pca.xyz()), or an object of class pdbs as obtained from function pdbaln or read.fasta.pdb (function pca.pdbs()).

Details

Principal component analysis can be performed on any structure dataset of equal or unequal sequence composition to capture and characterize inter-conformer relationships.

This generic pca function calls the corresponding methods function for actual calculation, which is determined by the class of the input argument x. Use methods("pca") to list all the current methods for pca generic. These will include:

pca.xyz, which will be used when x is a numeric matrix containing Cartesian coordinates (e.g. trajectory data).

pca.pdbs, which will perform PCA on the Cartesian coordinates of a input pdbs object (as obtained from the ‘read.fasta.pdb’ or ‘pdbaln’ functions).

Currently, function pca.tor should be called explicitly as there are currently no defined ‘tor’ object classes.

See the documentation and examples for each individual function for more details and worked examples.

References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.

Author

Barry Grant, Lars Skjaerven

See also

pca.xyz, pca.pdbs, pdbaln.