plotOptimResultsPan {CellNOptR} | R Documentation |
This function plots the data and simulated values according to each experiment in CNOlist. The data is shown as black triangles and the simulation by a blue dashed line. The combination of cues is given by a panel where black denotes the presence and white the absence of the cue. The goodness-of-fit between model and data is color-coded on a continuous scale from white to red.
plotOptimResultsPan(simResults, yInterpol=NULL, xCoords=NULL, CNOlist=CNOlist, formalism=c("ss1","ss2","ssN","dt","ode"), pdf=FALSE, pdfFileName="", tPt=NULL, plotParams = list(margin = 0.1, width=15, height=12, cmap_scale=1, cex=1.6, ymin=NULL, F=1, rotation=0))
simResults |
A list with a field for each time point, each containing a matrix of dimensions (number of conditions) * (number of signals), with the first field being t0. Typically produced by simulating a model and then extracting the columns that correspond to signals. |
yInterpol |
If using CNORdt, these are the interpolated experimental results from getFitTimeScale() that are needed to compare against the Boolean simulation. |
xCoords |
These are the x-coordinates obtained from the optimized scaling factor in CNORdt that allow for comparison between time course experimental data and a Boolean model. |
CNOlist |
A CNOlist. |
formalism |
An abbreviation of the CellNOptR formalism being used. |
pdf |
A Boolean argument denoting whether to print the figure produced by this function to file. |
pdfFileName |
If printing to file, the filename to be used. |
tPt |
The number of time points in the data. |
plotParams |
a list of option related to the PDF and plotting outputs. Currently, the following attributes are used: (1) margin of the boxes, (2) width and heigth used while creating the PDF, (3) cmap_scale a value that scales the colors towards small errors (<1) or large errors (>1); default is 1 (linear colormap) (4) cex is the fontsize used in the header (5) ymin sets the minimum y axis limit; by default it is the minimum value found over all data points and therefore can be negative. |
Depending on the logic formalism, this function is generally called from cutAndPlotResults*(). As shown in the example below however, it can plot the fit of any data and corresponding compatible model. The color denotes the goodness-of-fit, where white shows no difference between simulation and data and red is the maximum error from all conditions and readouts.
This function does not return a value.
A. MacNamara
J. Saez-Rodriguez, L. G. Alexopoulos, J. Epperlein, R. Samaga, D. A. Lauffenburger, S. Klamt and P. K. Sorger. Discrete logic modeling as a means to link protein signaling networks with functional analysis of mammalian signal transduction, Molecular Systems Biology, 5:331, 2009.
cutAndPlotResultsT1
data(CNOlistToy,package="CellNOptR") data(ToyModel,package="CellNOptR") indicesToy <- indexFinder(CNOlistToy, ToyModel, verbose=TRUE) ToyFields4Sim <- prep4sim(ToyModel) # simulate model simRes <- simulatorT1(CNOlist=CNOlistToy, model=ToyModel, simList=ToyFields4Sim, indexList=indicesToy) simRes = simRes[, indicesToy$signals] # format data and results simResults <- list(t0=matrix(data=0, nrow=dim(simRes)[1], ncol=dim(simRes)[2]), t1=simRes) # plot plotOptimResultsPan(simResults, CNOlist=CNOlistToy, formalism="ss1", pdf=FALSE, tPt=10 )