bio3d (version 2.1-1)

identify.cna: Identify Points in a CNA Protein Structure Network Plot

Description

identify.cna reads the position of the graphics pointer when the (first) mouse button is pressed. It then searches the coordinates given in x for the point closest to the pointer. If this point is close enough to the pointer, its index and community members will be returned as part of the value of the call and the community members will be added as labels to the plot.

Usage

## S3 method for class 'cna':
identify(x, labels=NULL, cna=NULL, ...)

Arguments

x

A numeric matrix with Nx2 dimensions, where N is equal to the number of objects in a 2D CNA plot such as obtained from the plot.cna and various layout functions.

labels

An optional character vector giving labels for the points. Will be coerced using as.character, and recycled if necessary to the length of x. Excess labels will be discarded, with a warning.

cna

A network object as returned from the cna function.

...

Extra options passed to identify function.

Value

• If labels or cna inputs are provided then a membership vector will be returned with the selected community ids and their members. Otherwise a vector with the ids of the selected communities will be returned.

Details

This function calls the identify and summary.cna functions to query and label 2D CNA protein structure network plots produced by the plot.cna function. Clicking with the mouse on plot points will add the corresponding labels and them to the plot and returned list object. A click with the right mouse button will stop the function.

See Also

plot.cna, identify, plot.igraph, plot.communities, igraph.plotting

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,"///24:27,85:90///CA/")

# lsq fit of trj on pdb
xyz <- fit.xyz(pdb$xyz, trj, fixed.inds=inds$xyz, mobile.inds=inds$xyz)

# calculate dynamical cross-correlation matrix
cij <- dccm(xyz)

# Build, and betweeness cluster, a network graph 
net <- cna(cij)

# Plot the network
xy <- plot.cna(net)

# Use identify.cna on the communities
d <- identify.cna(xy, cna=net)

# Right click to end the function...
## d <- identify(xy, summary(net)$members)