findgenes {gaga} | R Documentation |
Obtains a list of differentially expressed genes using the posterior
probabilities from a GaGa or MiGaGa fit. For parametric==TRUE
the procedure
controls the Bayesian FDR below fdrmax
. For
parametric==FALSE
it controls the estimated frequentist FDR.
findgenes(gg.fit, x, groups, fdrmax=.05, parametric=TRUE, B=500)
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. |
fdrmax |
Upper bound on FDR. |
parametric |
Set to TRUE to use the Bayes rule. Set to
FALSE to estimate the frequentist FDR non-parametrically. |
B |
Number of boostrap samples to estimate FDR non-parametrically (ignored if parametric==TRUE ) |
The Bayes rule to minimize expected FNR subject to FDR
<=fdrmax
declares differentially expressed all genes with
posterior probability of being equally expressed below a certain
threshold. The value of the threshold is computed exactly for
parametric==TRUE
, FDR being defined in a Bayesian sense. For
parametric==FALSE
the FDR is defined in a frequentist sense.
List with components:
efp |
Expected number of true positives. |
d |
Vector indicating the pattern that each gene is assigned to. |
fdr |
Frequentist estimated FDR that is closest to fdrmax. |
fdrpar |
Bayesian FDR. If parametric==TRUE , this is equal to fdrmax . If parametric==FALSE , it's the Bayesian FDR needed to achieve frequentist estimated FDR=fdrmax . |
fdrest |
Data frame with estimated frequentist FDR for each target Bayesian FDR |
fnr |
Bayesian FNR |
power |
Bayesian power as estimated by expected number of true positives divided by the expected number of differentially expressed genes |
threshold |
Optimal threshold for posterior probability of equal expression (genes with probability < threshold are declared DE) |
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) # #d <- findgenes(ggfit,xsim$x[,c(-6,-12)],groups,fdrmax=.05,parametric=TRUE) #dtrue <- (xsim$l[,1]!=xsim$l[,2]) #table(d$d,dtrue)