sim.plot.pvals.on.genome {SIM} | R Documentation |
Generates a plot of the analyzed dependent data probe positions and their significance on all chromosomes.
sim.plot.pvals.on.genome(input.regions = "all chrs", significance = c(0.05, 0.20), adjust.method = "BY", method = c("full", "smooth", "window", "overlap"), run.name = "analysis_results", pdf = TRUE, main = "Significantly associated features", ylab = "Chromosomes", ann=par("ann"), ...)
input.regions |
|
significance |
Two values that categorize the P-values on the selected chromosomes.
These margins are indicated by different colors shown in the legend.
These values can be defined, e.g. |
adjust.method |
Method used to adjust the P-values for multiple testing. see p.adjust Default is “BY” recommended when copy number is used as dependent data. See SIM for more information about adjusting P-values. |
method |
this must be the either full, window, overlap or smooth but the data should generated by the
same method in |
pdf |
Boolean. Indicate whether to generate a pdf of the plots in the current working directory or not. |
run.name |
This must be the same a given to |
main |
the usual graphical parameter for the caption of the plot. |
ylab |
the usual graphical parameter for the y-axis label of the plot. |
ann |
the usual graphical parameter for annotation of the plot. |
... |
Arguments to be passed to |
Grey vertical lines indicate unsignificant probes on top the significant ones are plotted. A purple dot indicates the centromere and a organe line the input region.
Sometimes it is useful to make the genome-plot as A4 landscape-format, add the following parameters to the
sim.plot.pvals.on.genome(..., paper='a4r', width=0, height=0)
No values are returned. The results are stored in the folder “pvalue.plots” in directory run.name
as pdf.
Marten Boetzer, Melle Sieswerda, Renee X. de Menezes R.X.Menezes@lumc.nl
SIM, sim.plot.zscore.heatmap, sim.plot.pvals.on.region
#first run example(assemble.data) #and example(integrated.analysis) #plot the p-values along the genome sim.plot.pvals.on.genome(input.regions="8q", significance=c(0.05, 0.005), adjust.method="BY", method="full", pdf=FALSE, run.name="chr8q")