DCCM Plot

Plot a dynamical cross-correlation matrix.

# S3 method for dccm
plot(x, resno=NULL, sse=NULL, colorkey=TRUE,
                    at=c(-1, -0.75, -0.5,  -0.25, 0.25, 0.5, 0.75, 1),
                    main="Residue Cross Correlation",
                    helix.col = "gray20", sheet.col = "gray80",
                    inner.box=TRUE, outer.box=FALSE,
                    xlab="Residue No.", ylab="Residue No.",
                    margin.segments=NULL, segment.col=vmd_colors(), segment.min=1, ...)

Arguments

x	a numeric matrix of atom-wise cross-correlations as output by the ‘dccm’ function.
resno	an optional vector with length equal to that of `x` that will be used to annotate the x- and y-axis. This is typically a vector of residue numbers. Can be also provided with a ‘pdb’ object, in which ‘resno’ of all C-alpha atoms will be used. If NULL residue positions from 1 to the length of `x` will be used. See examples below.
sse	secondary structure object as returned from `dssp`, `stride` or `read.pdb`.
colorkey	logical, if TRUE a key is plotted.
at	numeric vector specifying the levels to be colored.
main	a main title for the plot.
helix.col	The colors for rectangles representing alpha helices.
sheet.col	The colors for rectangles representing beta strands.
inner.box	logical, if TRUE an outer box is drawn.
outer.box	logical, if TRUE an outer box is drawn.
xlab	a label for the x axis.
ylab	a label for the y axis.
margin.segments	a numeric vector of cluster membership as obtained from cutree() or other community detection method. This will be used for bottom and left margin annotation.
segment.col	a vector of colors used for each cluster group in margin.segments.
segment.min	a single element numeric vector that will cause margin.segments with a length below this value to be excluded from the plot.
...	additional graphical parameters for contourplot.

Details

See the ‘contourplot’ function from the lattice package for plot customization options, and the functions dssp and stride for further details.

Value

Called for its effect.

References

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

Author

Barry Grant

Note

Be sure to check the correspondence of your ‘sse’ object with the ‘cij’ values being plotted as no internal checks are currently performed.

Examples


if (FALSE) {
  ##-- Read example trajectory file
  trtfile <- system.file("examples/hivp.dcd", package="bio3d")
  trj <- read.dcd(trtfile)

  ## Read reference PDB and trim it to match the trajectory
  pdb <- trim(read.pdb("1W5Y"), 'calpha')

  ## select residues 24 to 27 and 85 to 90 in both chains
  inds <- atom.select(pdb, resno=c(24:27,85:90))

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

  ## Dynamic cross-correlations of atomic displacements
  cij <- dccm(xyz)

  ## Default plot
  plot.dccm(cij)

  ## Change the color scheme and the range of colored data levels
  plot.dccm(cij, contour=FALSE, col.regions=bwr.colors(200), at=seq(-1,1,by=0.01) )

  ## Add secondary structure annotation to plot margins
  plot.dccm(cij, sse=pdb) 

  ## Add additional margin annotation for chains
  ## Also label x- and y-axis with PDB residue numbers
  ch <- ifelse(pdb$atom$chain=="A", 1,2)
  plot.dccm(cij, resno=pdb, sse=pdb, margin.segments=ch)

  ## Plot with cluster annotation from dynamic network analysis
  #net <- cna(cij)
  #plot.dccm(cij, margin.segments=net$raw.communities$membership)

  ## Focus on major communities (i.e. exclude those below a certain total length)
  #plot.dccm(cij, margin.segments=net$raw.communities$membership, segment.min=25)

}