geneclus: Cluster genes into expression patterns.
In gaga: GaGa hierarchical model for high-throughput data analysis

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

Performs supervised gene clustering. Clusters genes into the expression pattern with highest posterior probability, according to a GaGa or MiGaGa fit.

1	geneclus(gg.fit, method='posprob')

`gg.fit`	GaGa or MiGaGa fit (object of type `gagafit`, as returned by `fitGG`).
`method`	For `method=='posprob'` samples are assigned to pattern with highest posterior probability, and for `method=='likelihood'` to the pattern with highest likelihood (e.g. assuming equal a priori prob for all patterns)

Each gene is assigned to the pattern with highest posterior probability. This is similar to routine findgenes, which also assigns genes to the pattern with highest posterior probability, although findgenes applies an FDR-based correction i.e. tends to assign more genes to the null pattern of no differential expression.

List with components:

`d`	Vector indicating the pattern that each gene is assigned to.
`posprob`	Vector with posterior probabilities of the assigned patterns.

David Rossell

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

fitGG, parest

#Not run. Example from the help manual
#library(gaga)
#set.seed(10)
#n <- 100; m <- c(6,6)
#a0 <- 25.5; nu <- 0.109
#balpha <- 1.183; nualpha <- 1683
#probpat <- c(.95,.05)
#xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha)
#
#ggfit <- fitGG(xsim$x[,c(-6,-12)],groups,patterns=patterns,nclust=1)
#ggfit <- parest(ggfit,x=xsim$x[,c(-6,-12)],groups,burnin=100,alpha=.05)
#
#dclus <- geneclus(ggfit)  #not use FDR correction
#dfdr <- findgenes(ggfit,xsim$x[,c(-6,-12)],groups,fdrmax=.05,parametric=TRUE) #use FDR correction
#table(dfdr$d,dclus$d) #compare results