bio3d (version 2.3-4)

dccm.pca: Dynamic Cross-Correlation from Principal Component Analysis

Description

Calculate the cross-correlation matrix from principal component analysis (PCA).

Usage

# S3 method for pca
dccm(x, pc = NULL, ncore = NULL, …)

Arguments

x

an object of class pca as obtained from function pca.xyz.

pc

numerical, indices of PCs to be included in the calculation. If all negative, PCs complementary to abs(pc) are included.

ncore

number of CPU cores used to do the calculation. By default (ncore = NULL), use all available cores detected.

…

additional arguments to cov2dccm.

Value

Returns a cross-correlation matrix.

Details

This function calculates the cross-correlation matrix from principal component analysis (PCA) obtained from pca.xyz of a set of protein structures. It is an alternative way to calculate correlation in addition to the conventional way from xyz coordinates directly. But, in this new way one can freely chooses the PCs to be included in the calculation (e.g. filter PCs with small eigenvalues).

References

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

See Also

pca.xyz, plot.dccm

Examples

# NOT RUN {
##-- Read example trajectory file
trtfile <- system.file("examples/hivp.dcd", package="bio3d")
trj <- read.dcd(trtfile)

## Read the starting PDB file to determine atom correspondence
pdbfile <- system.file("examples/hivp.pdb", package="bio3d")
pdb <- read.pdb(pdbfile)

## Select residues 24 to 27 and 85 to 90 in both chains
inds <- atom.select(pdb, resno=c(24:27,85:90), elety='CA')

## lsq fit of trj on pdb
xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz)

## Do PCA
pca <- pca.xyz(xyz)

## DCCM: only use first 10 PCs
cij <- dccm(pca, pc = c(1:10))

## Plot DCCM
plot(cij)

## DCCM: remove first 10 PCs
cij <- dccm(pca, pc = -c(1:10))

## Plot DCCM
plot(cij)
# }