plot.dccm | R Documentation |
Plot a dynamical cross-correlation matrix.
## S3 method for class '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, ...)
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
|
sse |
secondary structure object as returned from
|
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. |
See the ‘contourplot’ function from the lattice package for plot customization options, and the functions dssp
and stride
for further details.
Called for its effect.
Be sure to check the correspondence of your ‘sse’ object with the ‘cij’ values being plotted as no internal checks are currently performed.
Barry Grant
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
plot.bio3d
, plot.dmat
,
filled.contour
, contour
,
image
plot.default
, dssp
,
stride
## Not run:
##-- 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)
## End(Not run)
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