iter.crossval: Performance assessment of gene signatures by...
In survJamda: Survival Prediction by Joint Analysis of Microarray Gene Expression Data

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

Assess the performance of a gene signature derived from a single or merged data set by ten iterations of cross validation.

1 2	iter.crossval(data, surv, censor, ngroup = 10, plot.roc = 0, method = "none", zscore = 0, gn.nb = 100, gn.nb.display = 0)

`data`	Matrix of gene expression data.
`surv`	Vector of survival time.
`censor`	Vector of censoring status. 1 = event occurred, 0 = censored.
`ngroup`	An integer specifying the number of cross-validation folds.
`plot.roc`	An integer specifying whether the ROC curves should be plotted or not (1 or 0).
`method`	A character string specifying the feature selection method: "none" for top-ranking 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 `data` is a merged data set or 0 if `data` is a single data set.
`gn.nb`	An integer specifying the number of genes to select for gene signature.
`gn.nb.display`	An integer specifying the number of selected genes to display.

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.

Mean of AUC +/- standard deviation of AUC, geometric mean of HR(CI).

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,

## Single data set
data(gse4335)
data(gse4335pheno)

#And run the following script:
#iter.crossval(gse4335, gse4335pheno[,6], gse4335pheno[,5])

## To observe the frequency of the CYB5D1 gene selection, run the following script:
#iter.crossval(gse4335, gse4335pheno[,6], gse4335pheno[,5], gn.nb =1, gn.nb.display = 1)

## Merged data set
data(gse4335)
data(gse4335pheno)

data(gse1992)
data(gse1992pheno)

common.gene = intersect(colnames(gse4335), colnames(gse1992))

data = rbind(gse4335[,common.gene], gse1992[,common.gene])
surv = c(gse4335pheno[,6],gse1992pheno[,19])
censor = c(gse4335pheno[,5],gse1992pheno[,18])

#And run the following script:
#iter.crossval(data, surv,censor, zscore=1)

## The function is currently defined as
function(data,surv,censor,ngroup=10,plot.roc=0,method="none",zscore=0,gn.nb=100,gn.nb.display=0){

         require(survival)
         require(survivalROC)

         res = NULL

         file.name=deparse(substitute(data)) 
         if (plot.roc)
	         init.plot(file.name)

         data =data[!is.na(surv),]
         censor= censor[!is.na(surv)]
         surv= surv[!is.na(surv)]

         cat ("Iteration\tAUC\tHR(CI)\t\tP-val\n")
         for (i in 1:ngroup){
                  new.lst = cross.val.surv(data, surv, censor,ngroup, i, method, zscore,
                  gn.nb,gn.nb.display,plot.roc,p.list)
                  res = rbind (res, new.lst)
         }

         if(ngroup != length(surv)){
                  cat ("Avg AUC+/-SD\tHR(CI)\n")
                  if (plot.roc)
                           legend (0.55,0.1, legend = paste("AUC+/-SD =", sprintf("%.2f",
                           as.numeric(mean(res[,1],na.rm = T))), "+/-", sprintf("%.2f",
                           sd (res[,1],na.rm = T)), sep = " "),bty = "n")
                  cat (sprintf("%.2f",as.numeric(mean(res[,1], na.rm = T))),  "+/-",
                  sprintf("%.2f",sd (res[,1],na.rm = T)), "\t", gm(res[,2]), 
                  "(", sprintf("%.2f",ci.gm(res[,2])[1]), "-", sprintf("%.2f",
                  ci.gm(res[,2])[2]), ")\n", sep = "")
         }        
}