R: Dynamical Cross-Correlation Matrix from Principal Component...

dccm.pca {bio3d} | R Documentation

## 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)

[Package bio3d version 2.4-0 Index]