Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/sigCheckRandom.R
Performance of a signature is compared to performance of signatures composed of the same number of randomlyselected features.
1  sigCheckRandom(check, iterations=10)

check 
A 
iterations 
The number of random signatures the primary signature will be compared to. This should be at least 1,000 to compute a meaningful empirical pvalue 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 pvalue 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, leaveoneout cross validation will be used to calculate the classification performance for each random signature).
An empirical pvalue 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 "Random"
.
$tests
is the number of tests run (equal to iterations
.)
$rank
is the performance rank of the primary signature
within the performance of the random signatures.
$checkPval
is the empirical pvalue 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 (pvalues)
for each random signature on the validation samples, if survival data
were provided.
$trainingPvalsRandom
is a vector of performance scores (pvalues)
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.
$performanceRandom
is a vector of classification performance
scores for each random signature, each indicating the proportion
of validation samples correctly classified if a classifier was used.
Rory Stark
sigCheck
, sigCheckAll
,
sigCheckPermuted
, sigCheckKnown
,
sigCheckPlot
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  #Disable parallel so Bioconductor build won't hang
library(BiocParallel)
register(SerialParam())
library(breastCancerNKI)
data(nki)
nki < nki[,!is.na(nki$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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