SigCheck (version 1.0.2)

sigCheckPermuted: Check classification performance of signature on randomly permuted data

Description

Performance of a classification signature on intact data is compared to performance in permuted data, either by feature (expression values of each feature permuted across samples), samples (expression values of all features permuted within each sample), or categories (permuted assignment of samples to classification categories).

Usage

sigCheckPermuted(expressionSet, classes, signature,  annotation, validationSamples,  classifierMethod = svmI, nIterations = 10, classifierScore, toPermute="features")

Arguments

expressionSet

An ExpressionSet object containing the data to be checked, including an expression matrix, feature labels, and samples.

classes

Specifies which label is to be used to determine the classification categories (must be one of varLabels(expressionSet)). There should be only two unique values in expressionSet$classes.

signature

A vector of feature labels specifying which features comprise the signature to be checked. These feature labels should match values as specified in the annotation parameter (default is row names in the expressionSet). Alternatively, this can be a integer vector of feature indexes.

annotation

Character string specifying which featureData field should be used as the annotation. If missing, the row names of the expressionSet are used as the feature names.

validationSamples

Optional specification, as a vector of sample indices, of what samples in the should used for validation. If present, a classifier will be trained, using the specified signature and classification method, on the non-validation samples, and its performance evaluated by attempting to classify the validations samples. If missing, a leave-one-out (LOO) validation method will be used, where a separate classifier will be trained to classify each sample using the remaining samples.

classifierMethod

The MLInterfaces learnerSchema object indicating the machine learning method to use for classification. Default is svmI for linear Support Vector Machine classification. See MLearn for available methods.

nIterations

The number of permutations to test and compare classification outcomes.

classifierScore

A performance measure of the baseline classifier. Generally the classifierScore element of the result list returned by sigCheckClassifier. If missing, sigCheckClassifier will be called to establish baseline performance.

toPermute

Character string or vector of strings indicating what should be permuted. Allowable values:

• "features": the expression values for each feature will be permuted (permutation by row).

• "samples": the expression values for each sample will be permuted (permutation by column).

• "categories": the values in classes will be permuted.

Value

A list with six elements:

• $sigPerformance is the percentage of validationSamples correctly classified (or, in the LOO case, the percentage of total samples correctly classified by classifiers trained using the remaining samples.)
• $modePerformance is the percentage of validationSamples correctly classified by a "mode" classifier (or, in the LOO case, the percentage of total samples correctly classified by a "mode" classifier, which is equal the number of samples with the more-frequent category.) The "mode" classifier always predicts the category that appears most often in the training set. If the training set is balanced between categories, one category will always be predicted.
• $permute is a character string or string of character strings detailing what aspects of the data were permuted (equal to toPermute.)
• $tests is the number of tests run (equal to nIterations.)
• $rank is the performance rank of the primary signature classifier on the unpermuted dataset amongst the performance of the signature on permuted datasets.
• $performancePermuted is a vector of performance scores (proportion of the validation set correctly predicted) for each permuted dataset.

Details

Any combination of permuteFeatures, permuteSamples, and permuteCategories can be specified. Performance for each signature is determined by calling sigCheckClassifier.

See Also

sigCheck, sigCheckClassifier, sigCheckRandom, sigCheckKnown, MLearn

Examples

library(breastCancerNKI)
data(nki)
nki <- nki[,!is.na(nki$e.dmfs)]
data(knownSignatures)
results <- sigCheckPermuted(nki, classes="e.dmfs", 
                            signature=knownSignatures$cancer$VANTVEER, 
                            annotation="HUGO.gene.symbol", 
                            validationSamples=275:319,
                            toPermute="features")