Survival Prediction by Joint Analysis of Microarray Gene Expression Data

aprior | Calculate empirical hyper-prior values |

Beta.NA | Fit the L/S model in the presence of missing data values |

bprior | Calculate empirical hyper-prior values of Bayesian model |

build.design | Initiation to build the design matrix |

cal.cox.coef | Cox coefficient calculation. |

calPerformance.auc.plot | Assess the performance obtained from the merged data set by... |

calPerformance.merge.indep | Assess performance derived from the merged data set by... |

calPerformance.meta | Meta analysis of survival data |

calPerformance.single.indep | Performance assessment on single data sets using independent... |

ci.gm | Confidence interval of a Geometric mean |

ComBat | ComBat-adjusted microarray gene expression data |

combat.likelihood | Likelihood function. |

comb.surv.censor | Merge survival times and censoring status. |

compute.combat | Initiate ComBat adjustment |

cross.val.combat | Cross validation with ComBat adjustment |

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

design.mat | Build a design matrix |

det.batchID | Determine the batch ID of data sets. |

detFileName | Determine the name of a file. |

det.set.ind | Determine the indices of the training or testing set. |

det.set.meta | Split data for meta analysis. |

eval.merge.simulate | Performance evaluation by merging two simulated independent... |

eval.subset | Performance evaluation derived from a subset of a data set |

excl.missing | Exclude missing samples |

excl.missing.single.indep | Exclude missing samples prior to independent validation |

excl.samples | Exclude samples |

featureselection | Apply a feature selection |

featureselection.meta | Feature selection for meta analysis |

filter.absent | Filter absent calls |

generate.survival.data | Generate survival data. |

gm | Geometric Mean |

groups.cv | Split a data set for cross-validation |

init.plot | Start plotting |

int.eprior | Integration function to find nonparametric adjustments |

inv.normal | Apply the inverse normal method. |

iter.crossval | Performance assessment of gene signatures by... |

iter.crossval.combat | Merge data set by ComBat within cross-validation. |

iter.subset | Performance evaluation by subsetting data sets in 100... |

it.sol | Iterative solution for Empirical Bayesian method. |

L | Likelihood function. |

list.batch | Make a list of data batches. |

main.merge.indep.valid | Performance assessment of merged data sets by independent... |

main.process | main.process |

main.single.indep.valid | Independent validation of the performance of the gene... |

meta.main | Meta analysis of survival data. |

plotROC | Plot ROC curves related to different time points. |

plot.roc.curves | Plot ROC curves of the testing set normalized by a joint... |

plot.time.dep | Plot time-dependent ROC curves from 0 to 120 months. |

pool.zscores | Combine data for meta analysis. |

postmean | Estimated additive batch effect |

postvar | Estimated multiplicative batch effect |

pred.time.indep.valid | Prediction of survival time by independent validation. |

prepcombat | Combination of data sets prior to the application of ComBat. |

prepcombat.single.indep | Pair-wise combination of single data sets prior to the... |

prepzscore | Z-score normalization. |

prepzscore1 | Apply Z-score1 normalization. |

prepzscore2 | Apply Z-score2 normalization. |

proc.simulate | Simulate survival data. |

shuffle.samples | Shuffle samples. |

splitMerged.auc.plot | Determine the indices of the training and testing sets. |

splitMerged.indep | Merge the data sets by ComBat or Z-score1 normalization and... |

splitZscore2.auc.plot | Z-score2 normalization prior to AUC plot. |

splitZscore2.merge.indep | Merge data sets by Z-score2 normalization and assess the... |

survJamda-package | Survival Prediction by Joint Analysis of Microarray Gene... |

trim.dat | Trim the data. |

writeGeno | Reformat gene expression data for ComBat. |

writeSamples | Write batch samples for ComBat. |

znorm | Matrix Z-score normalization. |

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