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gaga (version 2.18.0)

GaGa hierarchical model for high-throughput data analysis

Description

Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package).

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Version

Version

2.18.0

License

GPL (>= 2)

Maintainer

David Rossell

Last Published

February 15th, 2017

Functions in gaga (2.18.0)

geneclus

Cluster genes into expression patterns.
classpred

Predict the class that a new sample belongs to.
buildPatterns

Build a matrix with all possible patterns given a number of groups where samples may belong to.
parest

Parameter estimates and posterior probabilities of differential expression for GaGa and MiGaGa model
plotForwSim

Plot forward simulation trajectories
posmeansGG

Gene-specific posterior means
powclasspred

Expected probability that a future sample is correctly classified.
print.gagaclus

Print an object of class gagaclus
forwsimDiffExpr

Forward simulation for differential expression.
simGG

Prior predictive simulation
powfindgenes

Power computations for differential expression
fitGG

Fit GaGa hierarchical model
dcgamma

Approximate gamma shape distribution
print.gagahyp

Print an object of class gagahyp
checkfit

Check goodness-of-fit of GaGa and MiGaGa models
getpar

Extract hyper-parameter estimates from a gagafit or nnfit object
simnewsamples

Posterior predictive simulation
print.gagafit

Print an object of class gagafit or nnfit
seqBoundariesGrid

Evaluate expected utility for parametric sequential stopping boundaries.
findgenes

Find differentially expressed genes after GaGa or Normal-Normal fit.