"plot"(x, pc = 1, resno = NULL, sse = NULL, mask.n = 0, ...)
pca.array
.read.pdb
to show residue number on the x- and y-axis.dssp
or stride
,
or a ‘pdb’ object as obtained from read.pdb
to show secondary
structural elements along x- and y-axis.plot.dccm
.Plot residue-residue matrix loadings of a particular PC that is obtained from a principal component analysis (PCA) of cross-correlation or distance matrices.
The function plots loadings (the eigenvectors) of PCA performed on a set of matrices
such as distance matrices from an ensemble of crystallographic structures
and residue-residue cross-correlations or covariance matrices derived from
ensemble NMA or MD simulation replicates (See pca.array
for detail).
Loadings are displayed as a matrix with dimension the same as the input matrices
of the PCA. Each element of loadings represents the proportion that the corresponding
residue pair contributes to the variance in a particular PC. The plot can be used
to identify key regions that best explain the variance of underlying matrices.
Skjaerven, L. et al. (2014) BMC Bioinformatics 15, 399. Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696.
attach(transducin) gaps.res <- gap.inspect(pdbs$ali) sse <- bounds.sse(pdbs$sse[1, gaps.res$f.inds]) # calculate modes modes <- nma(pdbs, ncore=NULL)Warning message: 3QI2_B might have missing residue(s) in structure: Fluctuations at neighboring positions may be affected.Details of Scheduled Calculation: ... 53 input structures ... storing 909 eigenvectors for each structure ... dimension of x$U.subspace: ( 915x909x53 ) ... coordinate superposition prior to NM calculation ... aligned eigenvectors (gap containing positions removed) ... estimated memory usage of final 'eNMA' object: 336.8 Mb | | | 0%# calculate cross-correlation matrices from the modes cijs <- dccm(modes, ncore=NULL)$all.dccm # do PCA on cross-correlation matrices pc <- pca.array(cijs) # plot loadings l <- plot.matrix.loadings(pc, sse=sse) l[1:10, 1:10][,1] [,2] [,3] [,4] [,5] [1,] 0.000000000 -0.005377719 0.01655510 -0.009447706 0.008181432 [2,] -0.005377719 0.000000000 0.06076979 0.043934620 0.057926142 [3,] 0.016555096 0.060769787 0.00000000 0.015485347 0.060869248 [4,] -0.009447706 0.043934620 0.01548535 0.000000000 0.064631162 [5,] 0.008181432 0.057926142 0.06086925 0.064631162 0.000000000 [6,] 0.013125145 0.048562595 0.06950852 0.081754215 0.097543055 [7,] 0.047085071 0.048491901 0.03190358 -0.017976973 0.004826396 [8,] 0.048831159 0.043642856 0.02577068 -0.021062632 -0.036810509 [9,] 0.031796875 0.044415020 0.03879917 0.019895220 0.044282772 [10,] 0.021439384 0.045968042 0.04997447 0.050303833 0.085480899 [,6] [,7] [,8] [,9] [,10] [1,] 0.01312514 0.047085071 0.04883116 0.03179688 0.02143938 [2,] 0.04856259 0.048491901 0.04364286 0.04441502 0.04596804 [3,] 0.06950852 0.031903576 0.02577068 0.03879917 0.04997447 [4,] 0.08175421 -0.017976973 -0.02106263 0.01989522 0.05030383 [5,] 0.09754306 0.004826396 -0.03681051 0.04428277 0.08548090 [6,] 0.00000000 -0.030343841 -0.05462225 0.07912561 0.08507858 [7,] -0.03034384 0.000000000 -0.24051981 -0.11044962 0.09159816 [8,] -0.05462225 -0.240519812 0.00000000 0.05255921 0.02506549 [9,] 0.07912561 -0.110449617 0.05255921 0.00000000 0.11784389 [10,] 0.08507858 0.091598164 0.02506549 0.11784389 0.00000000# plot loadings with elements 10-residue separated from diagonal masked plot.matrix.loadings(pc, sse=sse, mask.n=10)