Performance of a signature is compared to performance of signatures composed of the same number of randomly-selected features.
The number of random signatures the primary signature will be compared to. This should be at least 1,000 to compute a meaningful empirical p-value for comparative performance.
sigCheckRandom will select
iterations signatures, each consisting
of the same number of features as are in the primary signature
provided in the call to
sigCheck that created the
SigCheckObject sampled at random from all available features.
Each random signature will be evaluated in the same manner as the primary signature. If survival data were supplied, a survival analysis will be carried out on the validation samples, and a p-value computed as a performance measure. If no survival data are available, the training samples will be used to train a classifier, and the performance score will be percentage of validation samples correctly classified. (If no validation samples are provided, leave-one-out cross validation will be used to calculate the classification performance for each random signature).
An empirical p-value will be computed based on the percentile rank of the performance of the primary signature compared to a null distribution of the performance of the random signatures.
A result list with the following elements:
$checkType is equal to
$tests is the number of tests run (equal to
$rank is the performance rank of the primary signature
within the performance of the random signatures.
$checkPval is the empirical p-value computed using the scores
of the random signature as a null distribution. A value of zero indicates that
no random signatures performed as good or better than the primary signature.
$survivalPval represents the performance of the primary,
if survival data were provided.
$survivalPvalsRandom is a vector of performance scores (p-values)
for each random signature on the validation samples, if survival data
$trainingPvalsRandom is a vector of performance scores (p-values)
for each random signature on the training samples, if survival data
and separate validation samples were provided.
$sigPerformance is the proportion of validation samples
correctly classified by the primary signature if a classifier was used.
$modePerformance is the proportion of validation samples
correctly classified using a mode classifier.
$performanceRandom is a vector of classification performance
scores for each random signature, each indicating the proportion
of validation samples correctly classified if a classifier was used.
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#Disable parallel so Bioconductor build won't hang library(BiocParallel) register(SerialParam()) library(breastCancerNKI) data(nki) nki <- nki[,!is.na(nki$e.dmfs)] data(knownSignatures) ITERATIONS <- 5 # should be at least 20, 1000 for real checks ## survival analysis check <- sigCheck(nki, classes="e.dmfs", survival="t.dmfs", signature=knownSignatures$cancer$VANTVEER, annotation="HUGO.gene.symbol", validationSamples=150:319) randomResult <- sigCheckRandom(check, iterations=ITERATIONS) randomResult$checkPval sigCheckPlot(randomResult)
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