Performs principal components analysis (PCA) on biomolecular structure data.
pca(...)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()).
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.
Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.