Description Usage Arguments Details Value Author(s) References See Also Examples
Obtains a list of differentially expressed genes using the posterior
probabilities from a GaGa, MiGaGa or Normal-Normal fit. For parametric==TRUE
the procedure
controls the Bayesian FDR below fdrmax
. For
parametric==FALSE
it controls the estimated frequentist FDR
(only available for GaGa).
1 |
fit |
Either GaGa/MiGaGa fit (object of class |
x |
|
groups |
If |
fdrmax |
Upper bound on FDR. |
.
parametric |
Set to |
B |
Number of boostrap samples to estimate FDR non-parametrically (ignored if |
The Bayes rule to minimize posterior expected FNR subject to posterior
expected 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:
truePos |
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 |
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 < |
David Rossell
Rossell D. (2009) GaGa: a Parsimonious and Flexible Model for Differential Expression Analysis. Annals of Applied Statistics, 3, 1035-1051.
Yuan, M. and Kendziorski, C. (2006). A unified approach for simultaneous gene clustering and differential expression identification. Biometrics 62(4): 1089-1098.
Muller P, Parmigiani G, Robert C, Rousseau J. (2004) Journal of the American Statistical Association, 99(468): 990-1001.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | #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)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.