Principal Component Analysis

Performs principal components analysis (PCA) on an ensemble of PDB structures.

# S3 method for pdbs
pca(pdbs, core.find = FALSE, fit = FALSE, ...)

Arguments

pdbs	an object of class pdbs as obtained from function pdbaln or read.fasta.pdb.
core.find	logical, if TRUE core.find() function will be called to find core positions and coordinates of PDB structures will be fitted based on cores.
fit	logical, if TRUE coordinates of PDB structures will be fitted based on all CA atoms.
...	additional arguments passed to the method pca.xyz.

Details

The function pca.pdbs is a wrapper for the function pca.xyz, wherein more details of the PCA procedure are documented.

Value

Returns a list with the following components:

L

eigenvalues.

U

eigenvectors (i.e. the variable loadings).

z.u

scores of the supplied data on the pcs.

sdev

the standard deviations of the pcs.

mean

the means that were subtracted.

References

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

Author

Barry Grant, Lars Skjaerven and Xin-Qiu Yao

See also

pca, pca.xyz, pdbaln, nma.

Examples

attach(transducin)

#-- Do PCA ignoring gap containing positions
pc.xray <- pca(pdbs)

# Plot results (conformer plots & scree plot)
plot(pc.xray, col=annotation[, "color"])

detach(transducin)