dccm.enma | R Documentation |
Calculate the cross-correlation matrices from an ensemble of NMA objects.
## S3 method for class 'enma'
dccm(x, ncore = NULL, na.rm=FALSE, ...)
x |
an object of class |
ncore |
number of CPU cores used to do the calculation.
|
na.rm |
logical, if FALSE the DCCM might containt NA values
(applies only when the |
... |
additional arguments passed to |
This is a wrapper function for calling dccm.nma
on a collection
of ‘nma’ objects as obtained from function nma.pdbs
.
See examples for more details.
Returns a list with the following components:
all.dccm |
an array or list containing the correlation matrices for each ‘nma’ object. An array is returned when the ‘enma’ object is calculated with ‘rm.gaps=TRUE’, and a list is used when ‘rm.gaps=FALSE’. |
avg.dccm |
a numeric matrix containing the average correlation matrix. The average is only calculated when the ‘enma’ object is calculated with ‘rm.gaps=TRUE’. |
Lars Skjaerven
Wynsberghe. A.W.V, Cui, Q. Structure 14, 1647–1653. Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
nma
, dccm.nma
, plot.dccm
## Needs MUSCLE installed - testing excluded
if(check.utility("muscle")) {
## Fetch PDB files and split to chain A only PDB files
ids <- c("1a70_A", "1czp_A", "1frd_A", "1fxi_A", "1iue_A", "1pfd_A")
files <- get.pdb(ids, split = TRUE, path = tempdir())
## Sequence/Structure Alignement
pdbs <- pdbaln(files, outfile = tempfile())
## Normal mode analysis on aligned data
modes <- nma(pdbs)
## Calculate all 6 correlation matrices
cij <- dccm(modes)
## Plot correlations for first structure
plot.dccm(cij$all.dccm[,,1])
}
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