filter.dccm.Rd
This function builds various cij matrix for correlation network analysis
filter.dccm(x, cutoff.cij = NULL, cmap = NULL, xyz = NULL, fac = NULL, cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...)
x  A matrix (nXn), a numeric array with 3 dimensions (nXnXm), a list with m cells each containing nXn matrix, or a list with ‘all.dccm’ component, containing atomic correlation values, where "n" is the number of residues and "m" the number of calculations. The matrix elements should be in between 1 and 1. See ‘dccm’ function in bio3d package for further details. 

cutoff.cij  Threshold for each individual correlation value. If NULL, a guessed value will be used. See below for details. 
cmap  logical or numerical matrix indicating the contact map.
If logical and TRUE, contact map will be calculated with input

xyz  XYZ coordinates, or a ‘pdbs’ object obtained from

fac  factor indicating distinct categories of input correlation matrices. 
cutoff.sims  Threshold for the number of simulations with observed correlation
value above 
collapse  logical, if TRUE the mean matrix will be returned. 
extra.filter  Filter to apply in addition to the model chosen. 
...  extra arguments passed to function 
Returns a matrix of class "dccm" or a 3D array of filtered crosscorrelations.
Grant, B.J. et al. (2006) Bioinformatics 22, 26952696.
XinQiu Yao, Guido Scarabelli & Barry Grant
If cmap is TRUE or provided a numerical matrix, the function inspects a set of crosscorrelation matrices, or DCCM, and decides edges for correlation network analysis based on:
1. min(abs(cij)) >= cutoff.cij, or
2. max(abs(cij)) >= cutoff.cij && residues contact each other
based on results from cmap
.
Otherwise, the function filters DCCMs with cutoff.cij
and
return the mean of correlations present in at least
cutoff.sims
calculated matrices.
An internally guessed cuoff.cij
is used if cutoff.cij=NULL
is provided.
By default, the cutoff is determined by keeping 5% of all residue pairs connected.
if (FALSE) { # Example of transducin attach(transducin) gaps.pos < gap.inspect(pdbs$xyz) modes < nma.pdbs(pdbs, ncore=NULL) dccms < dccm.enma(modes, ncore=NULL) cij < filter.dccm(dccms, xyz=pdbs) # Example protein kinase # Select Protein Kinase PDB IDs ids < c("4b7t_A", "2exm_A", "1opj_A", "4jaj_A", "1a9u_A", "1tki_A", "1csn_A", "1lp4_A") # Download and split by chain ID files < get.pdb(ids, path = "raw_pdbs", split=TRUE) # Alignment of structures pdbs < pdbaln(files) # Sequence identity summary(c(seqidentity(pdbs))) # NMA on all structures modes < nma.pdbs(pdbs, ncore=NULL) # Calculate correlation matrices for each structure cij < dccm(modes) # Set DCCM plot panel names for combined figure dimnames(cij$all.dccm) = list(NULL, NULL, ids) plot.dccm(cij$all.dccm) # Filter to display only correlations present in all structures cij.all < filter.dccm(cij, cutoff.sims = 8, cutoff.cij = 0) plot.dccm(cij.all, main = "Consensus Residue Cross Correlation") detach(transducin) }