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