cp.plot: Plot log-posterior probability for the output of the...

In sequenza: Copy Number Estimation from Tumor Genome Sequencing Data

Description

This function uses the colorgram function from the package squash to plot log-posterior probability for the tested combinations of cellularity and ploidy

Usage

   cp.plot(cp.table, xlab = "Ploidy", ylab = "Cellularity",
           zlab = "Scaled rank LPP",
           colFn = colorRampPalette(c('white', 'lightblue')), ...)
   cp.plot.contours(cp.table, likThresh = c(0.95), alternative = TRUE,
                    col = palette(), legend.pos = "bottomright", pch = 18,
                    alt.pch = 3, ...)
   get.ci(cp.table, level = 0.95)

Arguments

cp.table	list, as output from baf.model.fit or mufreq.model.fit.
xlab	xlab parameter as in the function colorgram.
ylab	ylab parameter as in the function colorgram.
zlab	zlab parameter as in the function colorgram.
colFn	colFn parameter as in the function colorgram.
likThresh	vector of quantiles to define tresholds for the confindent regions.
alternative	boolean parameter, if TRUE the alternative solutions are computed and plotted.
col	vector of colors.
legend.pos	position for placing the legend.
pch	character used to indicate the point estimate.
alt.pch	if alternative is set to TRUE defines the character to indicate alternative solutions.
...	additional arguments accepted by the function colorgram for cp.plot, or contour for cp.plot.contours.
level	decimal value of the confidence interval

Value

The get.ci function returns a list with 6 items:

values.ploidy	matrix of ploidy values with respective posterior probability.
confint.ploidy	boundaries of the confidence interval of the estimated ploidy.
max.ploidy	point estimate of the ploidy value that has the maximum posterior probability.
values.cellularity	matrix of cellularity values with respective posterior probability.
confint.cellularity	boundaries of the confidence interval of the estimated cellularity.
max.cellularity	point estimate of the cellularity value that has the maximum posterior probability.

Examples

data(CP.example)
cp.plot(CP.example)
cp.plot.contours(CP.example, add = TRUE)

# Plot more contours
cp.plot(CP.example)
cp.plot.contours(CP.example, likThresh = c(0.95, 0.9999), add = TRUE)

# Return the 95% confidence interval
CP.example.ci <- get.ci(CP.example)
str(CP.example.ci)

sequenza documentation built on May 9, 2019, 5:04 p.m.