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SigCheck (version 2.4.0)

sigCheckPlot: Plot results of a signature check or set of checks

Description

Plots results of a signature check, as returned by sigCheckRandom, sigCheckKnown, sigCheckPermuted, or sigCheckAll.

Usage

sigCheckPlot(checkResults, classifier=FALSE, title, nolegend=FALSE, ...)

Arguments

checkResults
classifier
If a classifier was used in the original call to sigCheck, setting this to TRUE will result in a plot showing how the primary signature compares based on classification performance (rather than survival).
title
Title string for plot. If missing, a default plot title will be generated.
nolegend
IF TRUE, no legend will be included with the plot(s).
...
Additional arguments to be passed to the plot function.

Value

none

Details

For results based on survival analysis, the background distribution of p-values (in 1-log10() format) derived from the check (either random signatures, known signatures, or performance using permuted data) is plotted. Up to two vertical red lines are also plotted: a solid red line representing the performance of the primary signature/data, and a dotted red line representing a p-value of 0.05. One or both of these may be missing if their performance falls outside the range of the background distributions.

For results based on classification performance, the x-axis represents the range of classification performance scores computed in the check, and the y-axis representing how many times that score was obtained. In addition, vertical lines are plotted representing the classification performance of the originally specified signature (solid red line) and the performance of a classifier that always predicts the mode value of the training samples (dotted red line).

If the results of sigCheckAll is passed in, all four results plots are generated in a 2x2 grid.

See Also

sigCheck, sigCheckAll, sigCheckRandom, sigCheckKnown, sigCheckPermuted

Examples

Run this code
#Disable parallel so Bioconductor build won't hang
library(BiocParallel)
register(SerialParam())

library(breastCancerNKI)
data(nki)
nki <- nki[,!is.na(nki$e.dmfs)]
data(knownSignatures)
ITERATIONS <- 5 # should be at least 1000 for real checks

## survival analysis with separate training and validation using SVM
check <- sigCheck(nki, classes="e.dmfs", survival="t.dmfs",
                  signature=knownSignatures$cancer$VANTVEER,
                  annotation="HUGO.gene.symbol",
                  validationSamples=250:319,
                  scoreMethod="classifier", threshold=.33) 
                  
results <- sigCheckRandom(check,iterations=ITERATIONS)
par(mfrow=c(1,2))
sigCheckPlot(results)
sigCheckPlot(results, classifier=TRUE)

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