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survJamda (version 1.1.4)

Survival Prediction by Joint Analysis of Microarray Gene Expression Data

Description

Microarray gene expression data can be analyzed individually or jointly using merging methods or meta-analysis to predict patients' survival and risk assessment.

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Version

Install

install.packages('survJamda')

Monthly Downloads

20

Version

1.1.4

License

GPL (>= 2)

Maintainer

Haleh Yasrebi

Last Published

November 5th, 2015

Functions in survJamda (1.1.4)

list.batch

Make a list of data batches.
calPerformance.meta

Meta analysis of survival data
shuffle.samples

Shuffle samples.
design.mat

Build a design matrix
splitZscore2.auc.plot

Z-score2 normalization prior to AUC plot.
comb.surv.censor

Merge survival times and censoring status.
meta.main

Meta analysis of survival data.
calPerformance.single.indep

Performance assessment on single data sets using independent validation
excl.samples

Exclude samples
detFileName

Determine the name of a file.
det.set.meta

Split data for meta analysis.
main.single.indep.valid

Independent validation of the performance of the gene signatures derived from single data sets.
pool.zscores

Combine data for meta analysis.
combat.likelihood

Likelihood function.
ComBat

ComBat-adjusted microarray gene expression data
aprior

Calculate empirical hyper-prior values
det.batchID

Determine the batch ID of data sets.
iter.subset

Performance evaluation by subsetting data sets in 100 iterations
cross.val.combat

Cross validation with ComBat adjustment
Beta.NA

Fit the L/S model in the presence of missing data values
compute.combat

Initiate ComBat adjustment
prepcombat.single.indep

Pair-wise combination of single data sets prior to the application of ComBat and independent validation.
it.sol

Iterative solution for Empirical Bayesian method.
pred.time.indep.valid

Prediction of survival time by independent validation.
L

Likelihood function.
generate.survival.data

Generate survival data.
build.design

Initiation to build the design matrix
prepzscore

Z-score normalization.
prepzscore2

Apply Z-score2 normalization.
calPerformance.auc.plot

Assess the performance obtained from the merged data set by independent validation
gm

Geometric Mean
bprior

Calculate empirical hyper-prior values of Bayesian model
prepcombat

Combination of data sets prior to the application of ComBat.
int.eprior

Integration function to find nonparametric adjustments
plot.time.dep

Plot time-dependent ROC curves from 0 to 120 months.
writeGeno

Reformat gene expression data for ComBat.
groups.cv

Split a data set for cross-validation
iter.crossval

Performance assessment of gene signatures by cross-validation.
znorm

Matrix Z-score normalization.
cross.val.surv

Cross validation with or without Z-score normalization
splitZscore2.merge.indep

Merge data sets by Z-score2 normalization and assess the performance by independent validation.
main.merge.indep.valid

Performance assessment of merged data sets by independent validation
postvar

Estimated multiplicative batch effect
cal.cox.coef

Cox coefficient calculation.
excl.missing.single.indep

Exclude missing samples prior to independent validation
splitMerged.indep

Merge the data sets by ComBat or Z-score1 normalization and apply independent validation.
plotROC

Plot ROC curves related to different time points.
writeSamples

Write batch samples for ComBat.
featureselection

Apply a feature selection
inv.normal

Apply the inverse normal method.
eval.subset

Performance evaluation derived from a subset of a data set
ci.gm

Confidence interval of a Geometric mean
filter.absent

Filter absent calls
postmean

Estimated additive batch effect
det.set.ind

Determine the indices of the training or testing set.
eval.merge.simulate

Performance evaluation by merging two simulated independent data sets
excl.missing

Exclude missing samples
plot.roc.curves

Plot ROC curves of the testing set normalized by a joint analysis method.
featureselection.meta

Feature selection for meta analysis
calPerformance.merge.indep

Assess performance derived from the merged data set by independent validation
survJamda-package

Survival Prediction by Joint Analysis of Microarray Gene Expression Data
splitMerged.auc.plot

Determine the indices of the training and testing sets.
trim.dat

Trim the data.
iter.crossval.combat

Merge data set by ComBat within cross-validation.
prepzscore1

Apply Z-score1 normalization.
proc.simulate

Simulate survival data.
init.plot

Start plotting
main.process

main.process