| geneclus {gaga} | R Documentation |
Performs supervised gene clustering. Clusters genes into the expression pattern with highest posterior probability, according to a GaGa or MiGaGa fit.
geneclus(gg.fit, method='posprob')
gg.fit |
GaGa or MiGaGa fit (object of type gagafit, as
returned by fitGG). |
method |
For method==1 samples are assigned to pattern
with highest posterior probability, and for method==1 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.
#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