bio3d (version 2.3-4)

filter.dccm: Filter for Cross-correlation Matrices (Cij)

Description

This function builds various cij matrix for correlation network analysis

Usage

filter.dccm(x, cutoff.cij = 0.4, cmap = NULL, xyz = NULL, fac = NULL, 
      cutoff.sims = NULL, collapse = TRUE, extra.filter = NULL, ...)

Arguments

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. 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

XYZ coordinates for distance matrix calculation.

fac

factor indicating distinct categories of input correlation matrices.

cutoff.sims

Threshold for the number of simulations with observed correlation value above cutoff.cij for the same residue/atomic pairs. See below for details.

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 cmap.

Value

Returns a matrix of class "dccm" or a 3D array of filtered cross-correlations.

Details

If cmap is TRUE or provided a numerical matrix, the function inspects a set of cross-correlation 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.

References

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

See Also

cna, dccm, dccm.nma, dccm.xyz, cmap, plot.dccm

Examples

# NOT RUN {
# }
# NOT RUN {
# 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)
# }
# NOT RUN {
# }