posmeansGG: Gene-specific posterior means
In gaga: GaGa hierarchical model for high-throughput data analysis

Description Usage Arguments Details Value Author(s) References See Also

Computes posterior means for the gene expression levels using a GaGa or MiGaGa model.

1	posmeansGG(gg.fit, x, groups, sel, underpattern)

`gg.fit`	GaGa or MiGaGa fit (object of type `gagafit`, as returned by `fitGG`).
`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.
`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]`.
`underpattern`	Expression pattern assumed to be true (defaults to last pattern in `gg.fit$patterns`). Posterior means are computed under this pattern. For example, if only the null pattern that all groups are equal and the full alternative that all groups are different are considered, `underpattern=1` returns the posterior means under the assumption that groups are different from each other (`underpattern=0` returns the same mean for all groups).

The posterior distribution of the mean parameters actually depends on the gene-specific shape parameter(s), which is unknown. To speed up computations, a gamma approximation to the shape parameter posterior is used (see rcgamma for details) and the shape parameter is fixed to its mode a posteriori.

Matrix with mean expression values a posteriori, for each selected gene and each group. Genes are in rows and groups in columns.

David Rossell

Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.

fitGG for fitting GaGa and MiGaGa models, parest for computing posterior probabilities of each expression pattern.

gaga documentation built on Nov. 8, 2020, 5:49 p.m.