Principal Component Analysis
Performs principal components analysis (PCA) on an ensemble of PDB structures.
## S3 method for class 'pdbs' pca(pdbs, core.find = FALSE, fit = FALSE, ...)
pdbs |
an object of class |
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 |
The function pca.pdbs is a wrapper for the function
pca.xyz, wherein more details of the PCA procedure
are documented.
Returns a list with the following components:
L |
eigenvalues. |
U |
eigenvectors (i.e. the variable loadings). |
z.u |
scores of the supplied |
sdev |
the standard deviations of the pcs. |
mean |
the means that were subtracted. |
Barry Grant, Lars Skjaerven and Xin-Qiu Yao
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
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)
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