bio3d (version 2.2-4)

filter.cmap: Contact Map Consensus Filtering

Description

This function filters a tridimensional contact matrix (nXnXz, where n is the residue number and z is the simulation number) selecting only the contact present in at least p simulations, where p

Usage

filter.cmap(cm, cutoff.sims = dim(cm)[3])

Arguments

cm

A numeric array with 3 dimensions (nXnXz) containing binary contact values. "n" is the residue number, "z" the simulation number. The matrix elements should be 1 if two residues are in contact and 0 if they are not in contact.

cutoff.sims

A single element numeric vector corresponding to the minimum number of simulations a contact between two residues must be present. If not, it will be set to 0 in the output matrix.

Value

The output matrix is a nXn binary matrix (n = residue number). Elements equal to 1 correspond to residues in contact, elements equal to 0 to residues not in contact.

See Also

cmap

Examples

## Not run: 
#   ## need abind package
#   if(!require(abind)) {
#      install.packages("abind")
#      require(abind)
#   }
# 
#   ## load example data
#   pdbfile <- system.file("examples/hivp.pdb", package="bio3d")
#   pdb <- read.pdb(pdbfile)
# 
#   trtfile <- system.file("examples/hivp.dcd", package="bio3d")
#   trj <- read.dcd(trtfile, verbose=FALSE)
# 
#   ## split the trj example in two
#   num.of.frames <- dim(trj)[1]
#   trj1 <- trj[1:(num.of.frames/2),]
#   trj2 <- trj[((num.of.frames/2)+1):num.of.frames,]
# 
#   ## Lets work with Calpha atoms only
#   ca.inds <- atom.select(pdb, "calpha")
#   #noh.inds <- atom.select(pdb, "noh")
# 
#   ## calculate single contact map matrices
#   cm.1 <- cmap(trj1[,ca.inds$xyz], pcut=0.3, scut=0, dcut=7, mask.lower=FALSE)
#   cm.2 <- cmap(trj2[,ca.inds$xyz], pcut=0.3, scut=0, dcut=5, mask.lower=FALSE)
# 
#   ## create a 3D contact matrix from 3 simulations
#   cm.all <- abind(cm.1, cm.2, along=3)
# 
#   ## calculate average contact matrix
#   cm.filter <- filter.cmap(cm=cm.all, cutoff.sims=2)
# 
#   ## plot the result
#   par(pty="s", mfcol=c(1,3))
#   image(cm.1, col=c(NA,"black"))
#   image(cm.2, col=c(NA,"black"))
#   image(cm.filter, col=c(NA,"black"))
# ## End(Not run)