sequenza.fit
functionThis function uses the colorgram
function from the package
squash to plot log-posterior probability for the tested combinations of
cellularity and ploidy
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)
list, as output from baf.model.fit
or mufreq.model.fit
.
xlab parameter as in the function colorgram
.
ylab parameter as in the function colorgram
.
zlab parameter as in the function colorgram
.
colFn parameter as in the function colorgram
.
vector of quantiles to define tresholds for the confindent regions.
boolean parameter, if TRUE
the alternative solutions are computed and plotted.
vector of colors.
position for placing the legend.
character used to indicate the point estimate.
if alternative
is set to TRUE
defines the character to indicate alternative solutions.
decimal value of the confidence interval
The get.ci
function returns a list with 6 items:
matrix of ploidy values with respective posterior probability.
boundaries of the confidence interval of the estimated ploidy.
point estimate of the ploidy value that has the maximum posterior probability.
matrix of cellularity values with respective posterior probability.
boundaries of the confidence interval of the estimated cellularity.
point estimate of the cellularity value that has the maximum posterior probability.
# NOT RUN {
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)
# }
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