plot.fluct. bio3d 2.3-0

Usage

"plot"(x, col = NULL, label = rownames(x), signif = FALSE, p.cutoff = 0.005, q.cutoff = 0.04, s.cutoff = 5, n.cutoff = 2, mean = FALSE, polygon = FALSE, spread = FALSE, offset = 1, ncore = NULL, ...)

Arguments

x: a numeric vector or matrix containing atomic fluctuation data obtained from e.g. nma.pdbs or rmsf.
col: a character vector of plotting colors. Used also to group fluctuation profiles. NA values in col will omit the corresponding fluctuation profile in the plot.
label: a character vector of plotting labels with length matching nrow(x). If mean=TRUE, the length of label can be equal to the number of categories indicated by col.
signif: logical, if TRUE significance of fluctuation difference is calculated and annotated for each atomic position.
p.cutoff: Cutoff of p-value to define significance.
q.cutoff: Cutoff of the mean fluctuation difference to define significance.
s.cutoff: Cutoff of sample size in each group to calculate the significance.
n.cutoff: Cutoff of consecutive residue positions with significant fluctuation difference. If the actual number is less than the cutoff, correponding postions will not be annotated.
mean: logical, if TRUE plot mean fluctuations of each group. Significance is still calculated with the original data.
polygon: logical, if TRUE a nicer plot with area under the line for the first row of x are filled with polygons.
ncore: number of CPU cores used to do the calculation. By default (ncore=NULL), use all available CPU cores. The argument is only used when signif=TRUE.
spread: logical, if TRUE the fluctuation profiles are spread - i.e. not on top of each other.
offset: numerical offset value in use when ‘spread=TRUE’.
...: extra plotting arguments passed to plot.bio3d.

Description

Produces a plot of atomic fluctuations obtained from ensemble normal mode analysis or molecular dynamics simulations.

Details

The significance calculation is performed when signif=TRUE and there are at least two groups with sample size larger than or equal to s.cutoff. A "two-sided" student's t-test is performed for each atomic position (each column of x). If x contains gaps, indicated by NAs, only non-gapped positions are considered. The position is considered significant if both p-value <= p.cutoff and the mean value difference of the two groups, q, satisfies q >= q.cutoff. If more than two groups are available, every pair of groups are subjected to the t-test calculation and the minimal p-value along with the q-value for the corresponding pair are used for the significance evaluation.

Value

If significance is calculated, return a vector indicating significant positions.

References

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

Examples

## load transducin example data
attach(transducin)

## subset of pdbs to analyze
inds = c(1:5, 16:20)
pdbs <- trim(pdbs, row.inds=inds)
gaps.res = gap.inspect(pdbs$ali)

## reference RESNO and SSE for axis annotations
resno <- pdbs$resno[1, gaps.res$f.inds]
sse <- pdbs$sse[1, gaps.res$f.inds]

## eNMA calculation and obtain modes of motion including atomic fluctuations
modes <- nma(pdbs, ncore=NULL)


Details of Scheduled Calculation:
  ... 10 input structures 
  ... storing 939 eigenvectors for each structure 
  ... dimension of x$U.subspace: ( 945x939x10 )
  ... coordinate superposition prior to NM calculation 
  ... aligned eigenvectors (gap containing positions removed)  
  ... estimated memory usage of final 'eNMA' object: 67.8 Mb 


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x = modes$fluctuation

## simple line plot with SSE annotation
plot.fluct(x, sse=sse, resno=resno)

## group data by specifying colors of each fluctuation line; same color indicates
## same group. Also do significance calculation and annotation
col = c(rep('red', 5), rep('blue', 5))


plot.fluct(x, col=col, signif=TRUE, sse=sse, resno=resno)



## spread lines
plot.fluct(x, col=col, signif=TRUE, sse=sse, resno=resno, typ='l', spread=TRUE)



## show only line of mean values for each group. 
## Nicer plot with area shaded for the first group.
plot.fluct(x, col=col, signif=TRUE, sse=sse, resno=resno, mean=TRUE, 
           polygon=TRUE, label=c('GTP', 'GDI'))



detach(transducin)

Author

Xin-Qiu Yao, Lars Skjaerven, Barry Grant

Plot Fluctuations