\name{plot-methods} \docType{methods} \alias{plot} \alias{plot-methods} \alias{plot,CCProfile,missing-method} \alias{plot,CCProfile,CCProfile-method} \alias{plot.CCProfile} \title{Plotting prediction profiles} \description{ Functions for plotting prediction profiles } \usage{ \S4method{plot}{CCProfile,missing}(x, col="red", rng=0, standardize=FALSE, shades=NULL, legend="", legend.pos="topright", xlab="", ylab="weight", ...) \S4method{plot}{CCProfile,CCProfile}(x, y, col=c("red", "blue"), rng=0, standardize=FALSE, shades=NULL, legend=NULL, legend.pos="topright", ...) } \arguments{ \item{x}{Object of class \code{\linkS4class{CCProfile}} to be plotted with \code{plot}} \item{y}{Object of class \code{\linkS4class{CCProfile}} to be plotted with \code{plot} (in case \code{plot} is called with two arguments to compare two profiles)} \item{col}{Character string containing the name of the color in which the profile should be plotted (in case \code{plot} is called with one \code{\linkS4class{CCProfile}} argument). A vector of character strings containing the names of the two colors in which the profiles should be plotted (in case \code{plot} is called with two \code{\linkS4class{CCProfile}} arguments).} \item{rng}{Argument that allows the user to preset the range of the profile plot. If 0 (default) or negative, the range is determined automatically from the values in the profile. Otherwise, the range is set to \eqn{[-rng,rng]}.} \item{standardize}{logical. If \code{FALSE} (default), the profile values \eqn{s_i}{si} are displayed as they are with the value \eqn{y=-b/L} superimposed as a light gray line. If \code{TRUE}, the whole profile is shifted by \eqn{-b/L} and the light gray line is displayed at \eqn{y=0}.} \item{shades}{Vector of at least two color specifications (default: NULL). If not NULL, the background area above and below the base line \eqn{y=-b/L} are shaded in colors \code{shades[1]} and \code{shades[2]}, respectively.} \item{legend}{A character string containing the legend/description of the profile (in case \code{plot} is called with one \code{\linkS4class{CCProfile}} argument). A vector of character strings containing the legends/descriptions of the profiles (in case \code{plot} is called with two \code{\linkS4class{CCProfile}} arguments). If empty, no legend is displayed.} \item{legend.pos}{Position specification for legend (if \code{legend} is specified). Can either be a vector with coordinates or a single keyword like \dQuote{topright} (see \code{\link[graphics:legend]{legend}}).} \item{xlab}{Label of horizontal axis, empty by default.} \item{ylab}{Label of vertical axis, defaults to \dQuote{weight}.} \item{...}{all other arguments are passed to the standard \code{\link[graphics:plot]{plot}} command that is called internally to display the graphics window} } \details{ The \code{plot} function displays the profile as a step function over the sequence with the steps connected by vertical lines. The vertical plot range can be determined by the \code{rng} argument. The sequence and the heptad register are visualized below and above the profile, respectively. The value \eqn{-b/L} and the light gray line has the following meaning: It is obvious that we can rewrite \deqn{f(x)=b+\sum\limits_{i=1}^{L} s_i(x)}{% f(x)=b+sum over all si(x) for i=1,\dots L} as \deqn{f(x)=\sum\limits_{i=1}^{L} (s_i(x) - (-\frac{b}{L}))}{% f(x)=sum over all (si(x) - (-b/L)) for i=1,\dots L,} so the discriminant function value \eqn{f(x)} can be understood as the sum of values \eqn{s_i(x) - (-\frac{b}{L})}{(si(x) - (-b/L))}, i.e. the area between the constant value \eqn{-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 \code{plot} is called with two \code{\linkS4class{CCProfile}} arguments, the two profiles are plotted together to facilitate a comparison of profiles (e.g. wild type sequences versus mutants). } \value{ Both variants of \code{plot} do not return any value. } \references{\url{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. DOI: 10.1074/mcp.M110.004994} \author{Ulrich Bodenhofer \email{bodenhofer@bioinf.jku.at}} \seealso{\code{\link{procoil}}, \code{\linkS4class{CCModel}}, \code{\linkS4class{CCProfile}}} \examples{ ## predict oligomerization of GCN4 wildtype GCN4wt<-predict(PrOCoilModel, "MKQLEDKVEELLSKNYHLENEVARLKKLV", "abcdefgabcdefgabcdefgabcdefga") ## plot profile plot(GCN4wt) ## predict oligomerization of a GCN4 mutant GCN4m3<-predict(PrOCoilModel, "MKQLEDKVEELLSKIYHNENEVARLKKLV", "abcdefgabcdefgabcdefgabcdefga") ## plot two profiles plot(GCN4wt, GCN4m3, legend=c("GCN4 wild type", "GCN4 mutant")) } % Add one or more standard keywords, see file 'KEYWORDS' in the % R documentation directory. \keyword{classif} \keyword{models} \keyword{methods}