Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/sigCheckPermuted.R
Performance of a signature on intact data is compared to performance on permuted data and/or metadata. Data may be permuted by by feature (expression values of each feature permuted across samples), samples (expression values of all features permuted within each sample). Metadata may be permuted by categories (permuted assignment of samples to classification categories) or survival (permuted assignment of survival times to samples).
1 | sigCheckPermuted(check, toPermute="categories", iterations=10)
|
check |
A |
toPermute |
Character string or vector of strings indicating what should be permuted. Allowable values:
|
iterations |
The number of permuted dataset the primary signature will be compared to. This should be at least 1,000 to compute a meaningful empirical p-value for comparative performance. |
The primary signature will be evaluated against each permuted dataset in the same manner as for the intact dataset.
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 permuted dataset).
An empirical p-value will be computed based on the percentile rank of the performance of the signature on the intact dataset compared to a null distribution of the performance of the signature on all the permuted datasets.
A result list with the following elements:
$checkType
is equal to "Permuted"
.
$permute
is equal to the passed value of toPermute
.
$tests
is the number of tests run (equal to iterations
.)
$rank
is the performance rank of the signature on the
intact dataset compared to its performance in the permuted datasets.
$checkPval
is the empirical p-value computed using the performance
scores of the signature on permuted datasets as a null distribution.
A value of zero indicates that the signature did not perform better on
any permuted datasets than it does using the intact data.
$survivalPval
represents the performance of the primary signature
on the original dataset
if survival data were provided.
$survivalPvalsPermuted
is a vector of performance scores (p-values)
for each permuted dataset, if survival data
were provided.
$trainingPvalsPermuted
is a vector of performance scores (p-values)
for each permuted dataset, if survival data
and separate validation samples were provided.
$sigPerformance
is the proportion of validation samples
correctly classified using the intact dataset if a classifier was used.
$modePerformance
is the proportion of validation samples
correctly classified in the intact dataset using a mode classifier.
$performancePermuted
is a vector of classification
performance scores for each
permuted dataset, each indicating the proportion
of validation samples correctly classified if a classifier was used.
Rory Stark
sigCheck
, sigCheckAll
,
sigCheckRandom
, sigCheckKnown
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | #Disable parallel so Bioconductor build won't hang
library(BiocParallel)
register(SerialParam())
library(breastCancerNKI)
data(nki)
nki <- nki[,!is.na(nki$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)
par(mfrow=c(1,2))
permutedCategories <- sigCheckPermuted(check, toPermute="categories",
iterations=ITERATIONS)
permutedCategories$checkPval
sigCheckPlot(permutedCategories)
permutedSurvival <- sigCheckPermuted(check, toPermute="survival",
iterations=ITERATIONS)
permutedSurvival$checkPval
sigCheckPlot(permutedSurvival)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.