dccm.pca {bio3d}

Dynamical Cross-Correlation Matrix from Principal Component Analysis

Description

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

Usage

## S3 method for class 'pca'
dccm(x, pc = NULL, method = c("pearson", "lmi"), 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.

method

method to calculate the cross-correlation. Currently supports Pearson and linear mutual information (LMI).

ncore

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

...

Additional arguments to be passed (currently ignored).

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. for filtering out PCs with small eigenvalues).

Value

Returns a cross-correlation matrix with values in a range from -1 to 1 (Pearson) or from 0 to 1 (LMI).

Author(s)

Xin-Qiu Yao

References

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

See Also

pca.xyz, plot.dccm, dccm, dccm.xyz, dccm.nma, dccm.enma.

Examples

##-- 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)