cross.val.combat: Cross validation with ComBat adjustment
In survJamda: Survival Prediction by Joint Analysis of Microarray Gene Expression Data

Description Usage Arguments Details Value Author(s) References See Also

Assess the performance of the gene signatures derived from the merged data set adjusted by ComBat in cross-validation.

1	cross.val.combat(x, y, censor, batchID, method, gn.nb, plot.roc, ngroup, iter)

`x`	Matrix of gene expression data.
`y`	Vector of survival time.
`censor`	Vector of censoring status. In the censoring status vector, 1 = event occurred, 0 = censored.
`batchID`	Vector containing the batch ID of the data set `x`. The batch ID of the data sets composing the matrix `x` should be in the same order of the component data sets. For a given data set, the batch id can be an integer or the name of the data set. The batch id must be the same for all samples or arrays of a data set.
`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.
`gn.nb`	An integer variable specifying the number of genes to select. The default is 100.
`plot.roc`	An integer specifying whether the ROC curves should be plotted or not (1 or 0).
`ngroup`	An integer variable specifying the number of cross-validation folds. The default is 10.
`iter`	An integer variable specifying the current number of iteration.

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.

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".

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.

Arithmetic mean of AUC +/- standard deviation and geometric mean of HR(CI) generated from cross-validation.

Haleh Yasrebi

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.

iter.crossval.combat

survJamda documentation built on May 1, 2019, 8:50 p.m.