nvalidate: Classification in a multiple random validation protocol in...

In cancerclass: Development and validation of diagnostic tests from high-dimensional molecular data

Description

Feature selection and class prediction in a multiple random validation protocol as it was introduced in [1]. Misclassifications rates are calculated for predictors that include different numbers of features.

Usage

nvalidate(eset, class="class", ngenes = c(5, 10, 20, 50, 100, 200, 500, 1000), method = "welch.test", dist="cor", ntrain ="balanced", nrep = 200, hparam = 0.75)

Arguments

eset	Bioconductor ExpressionSet
class	Specification of the column in pData(eset) that contains the class information.
ngenes	Numerical vector specifying the numbers features that are used for classification.
method	Character string specifying the feature selection method. Possible values are "cor", "student.test", "welch.test", "wilcoxon.test", "foldchange", "copa", "os", "ort", "shift", "throw".
dist	Character string specifying the method for calculation of the distance between test samples and the centroids. Possible values are "euclidean", "angle", "cor", "center".
ntrain	One of the strings "balanced" or "prevalence" or a numeric vector specifying the number of samples of class1 and the number of samples of class2 in the training sets.
nrep	The number of repetitions for each training set size.
hparam	Hyperparameter needed for some of the feature selection methods: Quantile used for the methods for copa, ort, os (e.g. 0.75, 0.95). Minimum number of samples in each class after applying shift/throw (only necessary for the feature selection methods: throw, shift).

Details

The matrix exprs(eset) contains the expression signatures of the patients in the columns. The character vector pData(eset)[[class]] contains the class membership of each sample or patient. Only tow-class problems are supported.

The hyperparameter hparam describes the minimum number of samples in each class after applying shift/throw. For copa, ort and os the hyperparameter specifies the quantile that has to be exceeded in order to consider a sample as an outlier. Typical values are 0.75 (default), 0.9, 0.95.

Validation is implemented in a multiple random validation protocol [1]. For each training set size, nrep training sets are randomly drawn from the patients. Features are selected and the centroid is calculated for each of the two classes in feature space. The test samples are classified to the class with the nearest centroid.

Four methods are available for calculation of the distance between test samples and the centroids: euclidean distance, euclidean distance after centering, angle and Pearson correlation. Calculation of distances is executed using the internal function get.d.

Feature selection, classification and validation are for predictors that include ngenes features.

Value

A nvalidation object, see nvalidation.object for details. Objects of this class have a method for the function plot.

References

[1] Michiels S, Koscielny S, Hill C (2005), Prediction of cancer outcome with microarrays: a multiple random validation strategy, Lancet 365:488-92.

See Also

nvalidation

Examples

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cancerclass documentation built on Nov. 8, 2020, 5:31 p.m.