bio3d (version 2.2-4)

pca.xyz: Principal Component Analysis

Description

Performs principal components analysis (PCA) on a xyz numeric data matrix.

Usage

"pca"(xyz, subset = rep(TRUE, nrow(as.matrix(xyz))), use.svd = FALSE, rm.gaps=FALSE, mass = NULL, ...)
"print"(x, nmodes=6, ...)

Arguments

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 xyz matrix. Note: the full xyz is projected onto this subspace.

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 pca, as obtained from function pca.xyz.

nmodes

numeric, number of modes to be printed.

mass

a ‘pdb’ object or numeric vector of residue/atom masses. By default (mass=NULL), mass is ignored. If provided with a ‘pdb’ object, masses of all amino acids obtained from aa2mass are used.

...

additional arguments to fit.xyz (for pca.xyz) or to print (for print.pca).

Value

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 xyz on the pcs.

au

atom-wise loadings (i.e. xyz normalized eigenvectors).

sdev

the standard deviations of the pcs.

mean

the means that were subtracted.

References

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

See Also

pca, pca.pdbs, plot.pca, mktrj.pca, pca.tor, project.pca

Examples

## 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)