Creates a matrix indicating which groups are put together under each pattern. The number of possible patterns increases very fast as the number of groups increases. This function provides an easy way to compute all possible patterns. The output of this function is usually used for the patterns parameter of the lmFit function.
● Data Source:
BioConductor
● Keywords: logic
● Alias: buildPatterns
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checkfit
(Package: gaga) :
Check goodness-of-fit of GaGa and MiGaGa models
Produces plots to check fit of GaGa and MiGaGa model. Compares observed data with posterior predictive distribution of the model. Can also compare posterior distribution of parameters with method of moments estimates.
● Data Source:
BioConductor
● Keywords: distribution, models
● Alias: checkfit, checkfit.gagafit
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classpred
(Package: gaga) :
Predict the class that a new sample belongs to.
Computes the posterior probability that a new sample belongs to each group and classifies it into the group with highest probability.
● Data Source:
BioConductor
● Keywords: htest, models
● Alias: classpred, classpred.gagafit
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dcgamma
(Package: gaga) :
Approximate gamma shape distribution
dcgamma approximates density of a gamma shape distribution with a gamma density. rcgamma obtains random draws from the approximation. mcgamma computes approximated mean, variance and normalization constant.
● Data Source:
BioConductor
● Keywords: distribution
● Alias: dcgamma, mcgamma, rcgamma
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findgenes
(Package: gaga) :
Find differentially expressed genes after GaGa or Normal-Normal fit.
Obtains a list of differentially expressed genes using the posterior probabilities from a GaGa, MiGaGa or Normal-Normal fit. For parametric==TRUE the procedure controls the Bayesian FDR below fdrmax. For parametric==FALSE it controls the estimated frequentist FDR (only available for GaGa).
● Data Source:
BioConductor
● Keywords: htest, models
● Alias: findgenes, findgenes.gagafit, findgenes.nnfit
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fitGG
(Package: gaga) :
Fit GaGa hierarchical model
fitGG fits GaGa/MiGaGa hierarchical models, either via a fully Bayesian approach or via maximum likelihood.
● Data Source:
BioConductor
● Keywords: models
● Alias: adjustfitNN, fitGG, fitNN, fitNNSingleHyp
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forwsimDiffExpr
(Package: gaga) :
Forward simulation for differential expression.
Forward simulation allows to evaluate the expected utility for sequential designs. Here the utility is the expected number of true discoveries minus a sampling cost. The routine simulates future data either from the prior predictive or using a set of pilot data and a GaGa or normal-normal model fit. At each future time point, it computes a summary statistic that will be used to determine when to stop the experiment.
● Data Source:
BioConductor
● Keywords: design, htest
● Alias: forwsimDiffExpr, forwsimDiffExpr.gagafit, forwsimDiffExpr.nnfit
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geneclus
(Package: gaga) :
Cluster genes into expression patterns.
Performs supervised gene clustering. Clusters genes into the expression pattern with highest posterior probability, according to a GaGa or MiGaGa fit.
● Data Source:
BioConductor
● Keywords: htest, models
● Alias: geneclus, geneclus.gagafit
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getpar
(Package: gaga) :
Extract hyper-parameter estimates from a gagafit or nnfit object
Extracts the hyper-parameter estimates from a gagafit or nnfit object and puts them in a list.
● Data Source:
BioConductor
● Keywords: models
● Alias: getpar, getpar.gagafit, getpar.nnfit
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parest
(Package: gaga) :
Parameter estimates and posterior probabilities of differential expression for GaGa and MiGaGa model
Obtains parameter estimates and posterior probabilities of differential expression after a GaGa or MiGaGa model has been fit with the function fitGG.
● Data Source:
BioConductor
● Keywords: models
● Alias: parest, parest.gagafit
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