Principal Component Analysis
Performs principal components analysis (PCA) on a xyz
numeric data matrix.
## S3 method for class 'xyz'
pca(xyz, subset = rep(TRUE, nrow(as.matrix(xyz))),
use.svd = FALSE, rm.gaps=FALSE, mass = NULL, ...)
## S3 method for class 'pca'
print(x, nmodes=6, ...)xyz |
numeric matrix of Cartesian coordinates with a row per structure. |
subset |
an optional vector of numeric indices that selects a
subset of rows (e.g. experimental structures vs molecular dynamics
trajectory structures) from the full |
use.svd |
logical, if TRUE singular value decomposition (SVD) is called instead of eigenvalue decomposition. |
rm.gaps |
logical, if TRUE gap positions (with missing coordinate data in any input structure) are removed before calculation. This is equivalent to removing NA cols from xyz. |
x |
an object of class |
nmodes |
numeric, number of modes to be printed. |
mass |
a ‘pdb’ object or numeric vector of residue/atom masses.
By default ( |
... |
additional arguments to |
Returns a list with the following components:
L |
eigenvalues. |
U |
eigenvectors (i.e. the x, y, and z variable loadings). |
z |
scores of the supplied |
au |
atom-wise loadings (i.e. xyz normalized eigenvectors). |
sdev |
the standard deviations of the pcs. |
mean |
the means that were subtracted. |
If mass is provided, mass weighted coordinates will be considered,
and iteration of fitting onto the mean structure is performed internally.
The extra fitting process is to remove external translation and rotation
of the whole system. With this option, a direct comparison can be made
between PCs from pca.xyz and vibrational modes from
nma.pdb, with the fact that
A=k[B]TF^-1
, where A is the variance-covariance matrix, F the Hessian matrix, k[B] the Boltzmann's constant, and T the temperature.
Barry Grant
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
## Not run:
#-- Read transducin alignment and structures
aln <- read.fasta(system.file("examples/transducin.fa",package="bio3d"))
pdbs <- read.fasta.pdb(aln)
# Find core
core <- core.find(pdbs,
#write.pdbs = TRUE,
verbose=TRUE)
rm(list=c("pdbs", "core"))
## End(Not run)
#-- OR for demo purposes just read previously saved transducin data
attach(transducin)
# Previously fitted coordinates based on sub 1.0A^3 core. See core.find() function.
xyz <- pdbs$xyz
#-- Do PCA ignoring gap containing positions
pc.xray <- pca.xyz(xyz, rm.gaps=TRUE)
# Plot results (conformer plots & scree plot overview)
plot(pc.xray, col=annotation[, "color"])
# Plot a single conformer plot of PC1 v PC2
plot(pc.xray, pc.axes=1:2, col=annotation[, "color"])
## Plot atom wise loadings
plot.bio3d(pc.xray$au[,1], ylab="PC1 (A)")
# PDB server connection required - testing excluded
## Plot loadings in relation to reference structure 1TAG
pdb <- read.pdb("1tag")
ind <- grep("1TAG", pdbs$id) ## location in alignment
resno <- pdbs$resno[ind, !is.gap(pdbs)] ## non-gap residues
tpdb <- trim.pdb(pdb, resno=resno)
op <- par(no.readonly=TRUE)
par(mfrow = c(3, 1), cex = 0.6, mar = c(3, 4, 1, 1))
plot.bio3d(pc.xray$au[,1], resno, ylab="PC1 (A)", sse=tpdb)
plot.bio3d(pc.xray$au[,2], resno, ylab="PC2 (A)", sse=tpdb)
plot.bio3d(pc.xray$au[,3], resno, ylab="PC3 (A)", sse=tpdb)
par(op)
## Not run:
# Write PC trajectory
resno = pdbs$resno[1, !is.gap(pdbs)]
resid = aa123(pdbs$ali[1, !is.gap(pdbs)])
a <- mktrj.pca(pc.xray, pc=1, file="pc1.pdb",
resno=resno, resid=resid )
b <- mktrj.pca(pc.xray, pc=2, file="pc2.pdb",
resno=resno, resid=resid )
c <- mktrj.pca(pc.xray, pc=3, file="pc3.pdb",
resno=resno, resid=resid )
## End(Not run)
detach(transducin)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.