cross.val.surv: Cross validation with or without Z-score normalization

Description

Assess the performance of the gene signatures derived from a single or merged data set by cross-validation.

Usage

cross.val.surv(x, y, censor, ngroup, iter, method, zscore, gn.nb, 
gn.nb.display, plot.roc)

Arguments

Matrix of gene expression data.

Vector of survival time.

censor

Vector of censoring status. In the censoring status vector, 1 = event occurred, 0 = censored.

ngroup

An integer specifying the number of cross-validation folds. The default is 10.

iter

An integer specifying the current number of iteration.

method

A character string specifying the feature selection method: "none" for top-ranking (top-100 ranking by default) or one of the adjusting methods specified by the p.adjust function.

zscore

An integer specifying whether Z-score normalization should be applied or not (1 or 0). 1 if the data is a merged data set and 0 if data is a single data set.

gn.nb

An integer specifying the number of genes to select. The default is 100.

gn.nb.display

An integer specifying the number of selected genes to display.

plot.roc

An integer specifying whether the ROC curves should be plotted or not (1 or 0).

Value

AUC and HR generated from cross-validation.

Details

The p.adjust function in the R stats package is used and all adjusted p-values not greater than 0.05 are retained if method != "none".

If the user wants to apply his own feature selection method, he should define his function with the same number of parameters as the defined feature selection function of the package, i.e. featureselection.

ROC curves are the plots of the mean of true positives (sensitivity) and the mean of false positives (1-specificity) over ngroup folds of cross-validation.

References

Yasrebi H, Sperisen P, Praz V, Bucher P, 2009 Can Survival Prediction Be Improved By Merging Gene Expression Data Sets?. PLoS ONE 4(10): e7431. doi:10.1371/journal.pone.0007431.