sigCheckRandom: Check signature performance against signatures composed of...

In SigCheck: Check a gene signature's prognostic performance against random signatures, known signatures, and permuted data/metadata

Description

Performance of a signature is compared to performance of signatures composed of the same number of randomly-selected features.

Usage

sigCheckRandom(check, iterations=10)

Arguments

check	A SigCheckObject, as returned by sigCheck.
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 p-value for comparative performance.

Details

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.

Value

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 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 were provided.

• $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.

Author(s)

Rory Stark

See Also

sigCheck, sigCheckAll, sigCheckPermuted, sigCheckKnown, sigCheckPlot

Examples

#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)

SigCheck documentation built on Nov. 8, 2020, 6:38 p.m.