plot-methods {procoil} | R Documentation

## Plotting prediction profiles

### Description

Functions for plotting prediction profiles

### Usage

    ## S4 method for signature 'CCProfile,missing'
    plot(x, col=c("red", "blue"),
         standardize=TRUE, shades=NULL, legend="default",
         legendPos="topright", xlab="", ylab="weight",
         lwd.profile=1, lwd.axis=1, las=1,
         heptads=TRUE, annotate=TRUE, ...)

### Arguments

`x` | Object of class `CCProfile` to be plotted with `plot`
`col` | Character string containing the name(s) of the color(s) in which the profile(s) should be plotted.
`standardize` | If `FALSE`, the profile values si are displayed as they are with the value y=-b/L superimposed as a light gray line. If `TRUE` (default), the profile(s) is/are shifted by the baseline values -b/L and the light gray line is displayed at y=0.
`shades` | Vector of at least two color specifications (default: NULL). If not NULL, the background area above and below the base line y=-b/L are shaded in colors `shades[1]` and `shades[2]`, respectively.
`legend` | A character string containing the legend/description of the profile. If `"default"`, the names of the sequences/profiles are used. If no names are available, the profiles are simply enumerated (as long as two profiles should be plot together; if only a single unnamed profile is to be plotted, no legend is shown). If `legend` is an empty string, no legend is displayed at all.
`legendPos` | position specification for legend (if `legend` is specified). Can either be a vector with coordinates or a single keyword like “topright” (see `legend`).
`xlab` | label of horizontal axis, empty by default.
`ylab` | label of vertical axis, defaults to “weight”.
`lwd.profile` | profile line width as described for parameter `lwd` in `par`
`lwd.axis` | axis line width as described for parameter `lwd` in `par`
`las` | see `par`
`heptads` | if `TRUE` (default), the heptad structure is indicated by vertical light gray lines separating the different heptads. Heptad irregularities are indicated with red lines.
`annotate` | if `TRUE` (default), the heptad annotation information is shown in the center of the plot.
`...` | all other arguments are passed to the `plot` method from the kebabs package

### Details

The `plot` function displays a prediction profile as a step function over the sequence with the steps connected by vertical lines. The sequence and the heptad register are visualized below and above the profile, respectively. The baseline value -b/L and the light gray line has the following meaning: It is obvious that we can rewrite

f(x)=b+sum over all si(x) for i=1,… L

as

f(x)=sum over all (si(x) - (-b/L)) for i=1,… L,

so the discriminant function value f(x) can be understood as the sum of values (si(x) - (-b/L)), i.e. the area between the constant value -b/L and the prediction profile. If the area above the light gray line is greater than the area below the light gray line, the sequence is predicted as trimer, otherwise as dimer.

If `plot` is called for a `CCProfile` object that contains profiles of two sequences, the two profiles are plotted together to facilitate a comparison of profiles (e.g. wild type sequences versus mutants). Although the `plot` function tolerates profiles/sequences with different lengths and/or unaligned heptad registers, it is obvious that the superimposition of profiles of two unaligned, unrelated sequences makes little sense.

The `plot` functions gives an error if is called for a `CCProfile` object that contains profiles of three or more sequences.

The given function is only a wrapper around the `plot` function provided by the kebabs package. The only difference is that heptad seperators (argument `heptads`) and the heptad annotation (argument `annotate`) are displayed by default. Moreover, presently, no legend is displayed by default if a single profile is plotted for an unnamed sequence.

### Value

This function does not return any value.

### Author(s)

Ulrich Bodenhofer bodenhofer@bioinf.jku.at

### References

http://www.bioinf.jku.at/software/procoil/

Mahrenholz, C.C., Abfalter, I.G., Bodenhofer, U., Volkmer, R., and Hochreiter, S. (2011) Complex networks govern coiled coil oligomerization - predicting and profiling by means of a machine learning approach. Mol. Cell. Proteomics 10(5):M110.004994. DOI: 10.1074/mcp.M110.004994

Palme, J., Hochreiter, S., and Bodenhofer, U. (2015) KeBABS: an R package for kernel-based analysis of biological sequences. Bioinformatics 31(15):2574-2576. DOI: 10.1093/bioinformatics/btv176

### See Also

`procoil`, `CCModel`, `CCProfile`

### Examples

    ## predict oligomerization of GCN4 wildtype
    GCN4wt <- predict(PrOCoilModel,
                      "MKQLEDKVEELLSKNYHLENEVARLKKLV",
                      "abcdefgabcdefgabcdefgabcdefga")

    ## plot profile
    plot(GCN4wt)

    ## define two GCN4 mutations
    GCN4mSeq <- c("GCN4wt"        ="MKQLEDKVEELLSKNYHLENEVARLKKLV",
                  "GCN4_N16I_L19N"="MKQLEDKVEELLSKIYHNENEVARLKKLV")
    GCN4mReg <- rep("abcdefgabcdefgabcdefgabcdefga", 2)

    ## predict oligomerization
    GCN4mut <- predict(PrOCoilModel, GCN4mSeq, GCN4mReg)

    ## overlay plot of the two profiles
    plot(GCN4mut)

[Package procoil version 2.16.0 Index]