gaga (version 2.18.0)

simnewsamples: Posterior predictive simulation

Description

Posterior and posterior predictive simulation for GaGa/MiGaGa and Normal-Normal models.

Usage

simnewsamples(fit, groupsnew, sel, x, groups)

Arguments

fit

Either GaGa or MiGaGa fit (object of type gagafit, as returned by fitGG) or Normal-Normal fit (type nnfit returned by fitNN).

groupsnew

Vector indicating the group that each new sample should belong to. length(groupsnew) is the number of new samples that will be generated.

sel

Numeric vector with the indexes of the genes we want to draw new samples for (defaults to all genes). If a logical vector is indicated, it is converted to (1:nrow(x))[sel]. For the Normal-Normal model this argument is ignored.

x

ExpressionSet, exprSet, data frame or matrix containing the gene expression measurements used to fit the model.

groups

If x is of type ExpressionSet or exprSet, groups should be the name of the column in pData(x) with the groups that one wishes to compare. If x is a matrix or a data frame, groups should be a vector indicating to which group each column in x corresponds to.

Value

Object of class 'ExpressionSet'. Expression values can be accessed via exprs(object) and the parameter values used to generate the expression values can be accessed via fData(object).

Details

For GaGa/MiGaGa models, the shape parameters are actually drawn from a gamma approximation to their posterior distribution. The function rcgamma implements this approximation.

In order to be consistent with the LNNGV model implemented in emfit (package EBarrays), for the Normal-Normal model the variance is drawn from an inverse gamma approximation to its marginal posterior (obtained by plugging in the group means, see EBarrays vignette for details).

References

Rossell D. (2009) GaGa: a Parsimonious and Flexible Model for Differential Expression Analysis. Annals of Applied Statistics, 3, 1035-1051.

Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.

See Also

checkfit for posterior predictive plot, simGG for prior predictive simulation.