plotTimeCourse {RPPanalyzer} | R Documentation |
Draw time course from RPPA data
Description
Draws time course data from a RPPA data list and calculates a mathematical model on the time course data.
Usage
plotTimeCourse(x, tc.identifier =
c("sample", "stimulation", "inhibition", "stim_concentration"),
tc.reference=NULL, plot.split = "experiment", file = NULL,
arrays2rm = c("protein", "Blank"), plotformat = "stderr",
log=TRUE, color=NULL, xlim = NULL, ylim = NULL)
Arguments
x |
List containing RPPA data set |
tc.identifier |
character string describing the column names in the sampledescription that identifies the individual time course experiments |
tc.reference |
character string describing the sample that will be used as reference for the time course plots. |
plot.split |
character string describing the column names in sampledescription that defines the argument that devides between different plots |
file |
character string for the name of the exported file |
arrays2rm |
character strings identifying the targets that should be from the time course plots |
plotformat |
character string defining the plot type: |
log |
logical, if true time courses signal intensities will be plotted at log2 scale |
color |
Vector holding the colors for the samples to be plot. If NULL, colors will be generated. |
xlim |
Limits for x-axis. If NULL (default) limits are generated for each timeseries plot. If a range (numeric vector of length 2) is given, this is used for all plots. |
ylim |
Analogous to |
Details
This function plots RPPA time course experiments from data sets with aggregated
replicate spots. A column time
containing numeric values is required in the sampledescription file.
One or several column in the sampledescription file should be
able to indentify the individual experiments described in argument tc.identifier
.
One column should provide a parameter plot.split
to split the whole data set into different
comparable time courses that have to be plotted together.
Different plotting options can be specified with the argument plotformat
. Option both
is
most informative, since it shows the original data plus standard deviations
at each time point, combined with a spline fit and the standard error
of the fit.
Value
generates a PDF file
Author(s)
Heiko Mannsperger <h.mannsperger@dkfz.de
Examples
## Not run:
library(RPPanalyzer)
data(dataII)
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="stderr")
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="errbar")
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="both")
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="rawdata")
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="spline")
plotTimeCourse(dataII,
tc.identifier=c("sample","stimulation","stim_concentration","inhibition")
,plot.split="experiment",plotformat="spline_noconf")
## End(Not run)