| Title: | GaGa hierarchical model for high-throughput data analysis |
|---|---|
| Description: | Implements the GaGa model for high-throughput data analysis, including differential expression analysis, supervised gene clustering and classification. Additionally, it performs sequential sample size calculations using the GaGa and LNNGV models (the latter from EBarrays package). |
| Authors: | David Rossell <[email protected]>. |
| Maintainer: | David Rossell <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 2.53.0 |
| Built: | 2024-11-30 05:18:50 UTC |
| Source: | https://github.com/bioc/gaga |
Creates a matrix indicating which groups are put together under each
pattern. The number of possible patterns increases very fast as the
number of groups increases. This function provides an easy way to
compute all possible patterns.
The output of this function is usually used for the patterns
parameter of the lmFit function.
buildPatterns(groups)buildPatterns(groups)
groups |
Character containing the names of the groups at which
samples may belong to.
If the output of the function is going to be used in |
buildPatterns(groups=c('GroupControl','GroupA','GroupB'))buildPatterns(groups=c('GroupControl','GroupA','GroupB'))
Produces plots to check fit of GaGa and MiGaGa model. Compares observed data with posterior predictive distribution of the model. Can also compare posterior distribution of parameters with method of moments estimates.
checkfit(gg.fit, x, groups, type='data', logexpr=FALSE, xlab, ylab, main, lty, lwd, ...)checkfit(gg.fit, x, groups, type='data', logexpr=FALSE, xlab, ylab, main, lty, lwd, ...)
gg.fit |
GaGa or MiGaGa fit (object of type |
x |
|
groups |
If |
type |
|
logexpr |
If set to |
xlab |
Passed on to |
ylab |
Passed on to |
main |
Passed on to |
lty |
Ignored. |
lwd |
Ignored. |
... |
Other arguments to be passed to |
The routine generates random draws from the posterior and posterior
predictive distributions, fixing the hyper-parameters at their
estimated value (posterior mean if model was fit with
method=='Bayes' or maximum likelihood estimate is model was fit
with method=='EBayes').
Produces a plot.
Posterior and posterior predictive checks can lack sensitivity to
detect model misfit, since they are susceptible to over-fitting. An
alternative is to perform prior predictive checks by generating
parameters and data with simGG.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
simGG to simulate samples from the
prior-predictive distribution, simnewsamples to generate parameters and
observations from the posterior predictive, which is useful to check
goodness-of-fit individually a desired gene.
Computes the posterior probability that a new sample belongs to each group and classifies it into the group with highest probability.
classpred(gg.fit, xnew, x, groups, prgroups, ngene=100)classpred(gg.fit, xnew, x, groups, prgroups, ngene=100)
gg.fit |
GaGa or MiGaGa fit (object of type |
xnew |
Expression levels of the sample to be classified. Only the subset of the genes indicated by ngene is used. |
x |
|
groups |
If |
prgroups |
Vector specifying prior probabilities for each group. Defaults to equally probable groups. |
ngene |
Number of genes to use to build the classifier. Genes with smaller probability of being equally expressed are selected first. |
The classifier weights each gene according to the posterior
probability that it is differentially expressed. Hence, adding genes
that are unlikely to be differentially expressed does not affect the
performance of the classifier, but it does increase the computational
cost.
All computations are performed by fixing the hyper-parameters to their
estimated value (posterior mean if model was fit with
method=='Bayes' or maximum likelihood estimate is model was fit
with method=='EBayes').
List with the following elements:
d |
Numeric value indicating the group that the new sample is
classified into, i.e. where the maximum in |
posgroups |
Vector giving the posterior probability that the
|
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) # #pred1 <- classpred(ggfit,xnew=xsim$x[,6],x=xsim$x[,c(-6,-12)],groups) #pred2 <- classpred(ggfit,xnew=xsim$x[,12],x=xsim$x[,c(-6,-12)],groups) #pred1 #pred2#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) # #pred1 <- classpred(ggfit,xnew=xsim$x[,6],x=xsim$x[,c(-6,-12)],groups) #pred2 <- classpred(ggfit,xnew=xsim$x[,12],x=xsim$x[,c(-6,-12)],groups) #pred1 #pred2
dcgamma approximates density of a gamma shape distribution with
a gamma density. rcgamma obtains random draws from the
approximation. mcgamma computes approximated mean, variance and
normalization constant.
dcgamma(x, a, b, c, d, r, s, newton = TRUE) rcgamma(n, a, b, c, d, r, s, newton = TRUE) mcgamma(a, b, c, d, r, s, newton = TRUE)dcgamma(x, a, b, c, d, r, s, newton = TRUE) rcgamma(n, a, b, c, d, r, s, newton = TRUE) mcgamma(a, b, c, d, r, s, newton = TRUE)
x |
Vector indicating the values at which to evaluate the density. |
n |
Number of random draws to obtain. |
a, b, c, d, r, s
|
Parameter values. |
newton |
Set to |
The density of a gamma shape distribution is given by
C(a,b,c,d,r,s) (gamma(a*x+d)/gamma(x)^a)
(x/(r+s*x))^{a*x+d} x^{b-d-1} exp(-x*c)
for x>=0, and 0 otherwise, where C() is the normalization constant.
The gamma approximation is
Ga(a/2+b-1/2,c+a*log(s/a)). The approximate normalization constant is
obtained by taking the ratio of the exact density and the
approximation at the maximum, as described in Rossell (2007).
dcgamma returns a vector with approximate density.
rcgamma returns a vector with draws from the approximating gamma.
mcgamma returns a list with components:
m |
Approximate mean |
v |
Approximate variance |
normk |
Approximate normalization constant |
For general values of the parameters the gamma approximation may be poor. In such a case one could use this function to obtain draws from the proposal distribution in a Metropolis-Hastings step.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
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).
findgenes(fit, x, groups, fdrmax=.05, parametric=TRUE, B=500)findgenes(fit, x, groups, fdrmax=.05, parametric=TRUE, B=500)
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.
#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)#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)
fitGG fits GaGa/MiGaGa hierarchical models, either via a fully Bayesian approach or via maximum likelihood.
fitNN fits a normal-normal hierarchical model (wrapper to call emfit in package EBarrays with the LNNMV model). fitNNSingleHyp is the same as fitNN but only considers the pattern that all groups are different from each other.
adjustfitNN corrects a small sample-size bias in the fitNN estimation procedure.
fitGG(x, groups, patterns, equalcv = TRUE, nclust = 1, method = "quickEM", B, priorpar, parini, trace = TRUE) fitNN(x, groups, patterns, B=20, trace=TRUE) fitNNSingleHyp(x, groups, B=10, trace=TRUE) adjustfitNN(fit, pitrue, B=5, nsim=3, mc.cores=1)fitGG(x, groups, patterns, equalcv = TRUE, nclust = 1, method = "quickEM", B, priorpar, parini, trace = TRUE) fitNN(x, groups, patterns, B=20, trace=TRUE) fitNNSingleHyp(x, groups, B=10, trace=TRUE) adjustfitNN(fit, pitrue, B=5, nsim=3, mc.cores=1)
x |
|
groups |
If |
patterns |
Matrix indicating which groups are put together under
each pattern, i.e. the hypotheses to consider for each
gene. |
equalcv |
|
nclust |
Number of clusters in the MiGaGa model. |
method |
|
B |
Number of iterations to fit the model. For |
priorpar |
List with prior parameter values. It must have
components |
parini |
list with components |
trace |
For |
fit |
|
pitrue |
Grid of true |
nsim |
Number of datasets to simulate for each |
mc.cores |
If package |
For GaGa/MiGaGa models, an approximation is used to sample faster from the
posterior distribution of the gamma shape parameters and to compute
the normalization constants (needed to evaluate the likelihood). These
approximations are implemented in rcgamma and mcgamma.
The cooling scheme in method=='SA' uses a temperature equal to
1/log(1+i), where i is the iteration number.
The EM implementation in method=='quickEM' is a quick EM
algorithm that usually delivers hyper-parameter estimates very similar
to those obtained via the slower method=='EM'. Additionally,
the GaGa model inference has been seen to be robust to moderate
changes in the hyper-parameter estimates in most datasets.
fitNN is a wrapper to emfit in package EBarrays with the LNNMV model.
This procedure estimates hyper-parameters using the method of moments
(MoM), which typically results in over-estimating the proportion of
differentially expressed genes, which we denote by pi1.
adjustfitNN corrects this bias by repeatedly simulating from
the prior predictive of a normal-normal model. Simulations are
performed for a grid of pi1 values, so that the expected bias can be
evaluated for each of them. The bias is then modeled as a smooth
function of pi1 using function gam from package mgcv.
Finally, the value of pi1 is bias-adjusted and the posterior
probabilities are recomputed using the updated pi1 value.
fitGG returns an object of class gagafit, with components
parest |
Hyper-parameter estimates. Only returned if |
mcmc |
Object of class |
lhood |
For |
nclust |
Same as input argument. |
patterns |
Same as input argument, converted to object of class
|
fitNN returns an analogous object of class nnfit. The
component nn.fit is the object returned by emfit.
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.
parest to estimate hyper-parameters and compute
posterior probabilities after a GaGa or MiGaGa
fit. findgenes to find differentially expressed
genes. classpred to predict the group that a new sample
belongs to.
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,equalcv=TRUE) x <- exprs(xsim) #Frequentist fit: EM algorithm to obtain MLE groups <- pData(xsim)$group[c(-6,-12)] patterns <- matrix(c(0,0,0,1),2,2) colnames(patterns) <- c('group 1','group 2') gg1 <- fitGG(x[,c(-6,-12)],groups,patterns=patterns,method='EM',trace=FALSE) gg1 <- parest(gg1,x=x[,c(-6,-12)],groups) gg1library(gaga) set.seed(10) n <- 100; m <- c(6,6) a0 <- 25.5; nu <- 0.109 balpha <- 1.183; nualpha <- 1683 probpat <- c(.95,.05) xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha,equalcv=TRUE) x <- exprs(xsim) #Frequentist fit: EM algorithm to obtain MLE groups <- pData(xsim)$group[c(-6,-12)] patterns <- matrix(c(0,0,0,1),2,2) colnames(patterns) <- c('group 1','group 2') gg1 <- fitGG(x[,c(-6,-12)],groups,patterns=patterns,method='EM',trace=FALSE) gg1 <- parest(gg1,x=x[,c(-6,-12)],groups) gg1
Forward simulation allows to evaluate the expected utility for sequential designs. Here the utility is the expected number of true discoveries minus a sampling cost. The routine simulates future data either from the prior predictive or using a set of pilot data and a GaGa or normal-normal model fit. At each future time point, it computes a summary statistic that will be used to determine when to stop the experiment.
forwsimDiffExpr(fit, x, groups, ngenes, maxBatch, batchSize, fdrmax = 0.05, genelimit, v0thre = 1, B = 100, Bsummary = 100, trace = TRUE, randomSeed, mc.cores=1)forwsimDiffExpr(fit, x, groups, ngenes, maxBatch, batchSize, fdrmax = 0.05, genelimit, v0thre = 1, B = 100, Bsummary = 100, trace = TRUE, randomSeed, mc.cores=1)
fit |
Either GaGa or MiGaGa fit (object of type |
x |
|
groups |
If |
ngenes |
Number of genes to simulate data for. If |
maxBatch |
Maximum number of batches, i.e. the routine simulates
|
batchSize |
Batch size, i.e. number of observations per group to
simulate at each time point. Defaults to |
fdrmax |
Upper bound on FDR. |
genelimit |
Only the |
v0thre |
Only genes with posterior probability of being equally
expressed < |
B |
Number of forward simulations. |
Bsummary |
Number of simulations for estimating the summary statistic. |
trace |
For |
randomSeed |
Integer value used to set random number generator
seed. Defaults to |
mc.cores |
If |
To improve computational speed hyper-parameters are not re-estimated as new data is simulated.
A data.frame with the following columns:
simid |
Simulation number. |
j |
Time (sample size). |
u |
Expected number of true positives if we were to stop experimentation at this time. |
fdr |
Expected FDR if we were to stop experimentation at this time. |
fnr |
Expected FNR if we were to stop experimentation at this time. |
power |
Expected power (as estimated by E(TP)/E(positives)) if we were to stop experimentation at this time. |
summary |
Summary statistic: increase in expected true positives if we were to obtain one more data batch. |
David Rossell.
Rossell D., Mueller P. Sequential sample sizes for high-throughput hypothesis testing experiments. http://sites.google.com/site/rosselldavid/home.
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. Annals of Applied Statistics, 2009, 3, 1035-1051.
plotForwSim to plot the simulated trajectories,
fitGG for fitting a GaGa model,
fitNN for fitting a normal-normal model,
seqBoundariesGrid for finding the optimal design based
on the forwards simulation output.
powfindgenes for fixed sample size calculations.
#Simulate data and fit GaGa model set.seed(1) x <- simGG(n=20,m=2,p.de=.5,a0=3,nu=.5,balpha=.5,nualpha=25) gg1 <- fitGG(x,groups=1:2,method='EM') gg1 <- parest(gg1,x=x,groups=1:2) #Run forward simulation fs1 <- forwsimDiffExpr(gg1, x=x, groups=1:2, maxBatch=2,batchSize=1,fdrmax=0.05, B=100, Bsummary=100, randomSeed=1) #Expected number of true positives for each sample size tapply(fs1$u,fs1$time,'mean') #Expected utility for each sample size samplingCost <- 0.01 tapply(fs1$u,fs1$time,'mean') - samplingCost*(0:2) #Optimal sequential design b0seq <- seq(0,20,length=200); b1seq <- seq(0,40,length=200) bopt <-seqBoundariesGrid(b0=b0seq,b1=b1seq,forwsim=fs1,samplingCost=samplingCost,powmin=0) bopt <- bopt$opt plot(fs1$time,fs1$summary,xlab='Additional batches',ylab='E(newly discovered DE genes)') abline(bopt['b0'],bopt['b1']) text(.2,bopt['b0'],'Continue',pos=3) text(.2,bopt['b0'],'Stop',pos=1)#Simulate data and fit GaGa model set.seed(1) x <- simGG(n=20,m=2,p.de=.5,a0=3,nu=.5,balpha=.5,nualpha=25) gg1 <- fitGG(x,groups=1:2,method='EM') gg1 <- parest(gg1,x=x,groups=1:2) #Run forward simulation fs1 <- forwsimDiffExpr(gg1, x=x, groups=1:2, maxBatch=2,batchSize=1,fdrmax=0.05, B=100, Bsummary=100, randomSeed=1) #Expected number of true positives for each sample size tapply(fs1$u,fs1$time,'mean') #Expected utility for each sample size samplingCost <- 0.01 tapply(fs1$u,fs1$time,'mean') - samplingCost*(0:2) #Optimal sequential design b0seq <- seq(0,20,length=200); b1seq <- seq(0,40,length=200) bopt <-seqBoundariesGrid(b0=b0seq,b1=b1seq,forwsim=fs1,samplingCost=samplingCost,powmin=0) bopt <- bopt$opt plot(fs1$time,fs1$summary,xlab='Additional batches',ylab='E(newly discovered DE genes)') abline(bopt['b0'],bopt['b1']) text(.2,bopt['b0'],'Continue',pos=3) text(.2,bopt['b0'],'Stop',pos=1)
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')geneclus(gg.fit, method='posprob')
gg.fit |
GaGa or MiGaGa fit (object of type |
method |
For |
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$d) #compare results#Not run. Example from the help manual #library(gaga) #set.seed(10) #n <- 100; m <- c(6,6) #a0 <- 25.5; nu <- 0.109 #balpha <- 1.183; nualpha <- 1683 #probpat <- c(.95,.05) #xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha) # #ggfit <- fitGG(xsim$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
Extracts the hyper-parameter estimates from a gagafit or
nnfit object
and puts them in a list.
getpar(fit)getpar(fit)
fit |
Object of class |
This routine simply evaluates the component parest from
a gagafit or nnfit object, which causes an error if this component
is not available. This routine is used internally by a number of other
routines.
For gagafit objects, a list with components:
a0 |
Estimated value of hyper-parameter |
nu |
Estimated value of hyper-parameter |
balpha |
Estimated value of hyper-parameter |
nualpha |
Estimated value of hyper-parameter |
probclus |
Estimated cluster probabilities |
probpat |
Estimated prior probability of each expression pattern |
For nnfit objects, a vector with elements mu0,
tau02, v0, sigma02, probclus and
probpat. These are the hierarchical N(mu0,tau0^2) *
IG(.5*v0,.5*v0*sigma0^2) prior parameter estimates.
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.
Obtains parameter estimates and posterior probabilities of
differential expression after a GaGa or MiGaGa model has been fit with
the function fitGG.
parest(gg.fit, x, groups, burnin, alpha=.05)parest(gg.fit, x, groups, burnin, alpha=.05)
gg.fit |
GaGa or MiGaGa fit (object of type |
x |
|
groups |
If |
burnin |
Number of MCMC samples to discard. Ignored if
|
alpha |
If |
If gg.fit was fit via MCMC posterior sampling (option
method=='Bayes'), parest discards the first
burnin iterations and uses the rest to obtain point estimates
and credibility intervals for the hyper-parameters.
To compute posterior probabilities of differential expression the hyper-parameters are fixed to
their estimated value, i.e. not averaged over MCMC iterations.
An object of class gagafit, with components:
parest |
Hyper-parameter estimates. |
mcmc |
Object of class |
lhood |
For |
nclust |
Number of clusters. |
patterns |
Object of class |
pp |
Matrix with posterior probabilities of differential expression for each gene. Genes are in rows and expression patterns are in columns (e.g. for 2 hypotheses, 1st column is the probability of the null hypothesis and 2nd column for the alternative). |
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
fitGG to fit a GaGa or MiGaGa model,
findgenes to find differentially expressed genes and
posmeansGG to obtain posterior expected expression values.
classpred performs class prediction.
#Not run #library(EBarrays); data(gould) #x <- log(exprs(gould)[,-1]) #exclude 1st array #groups <- pData(gould)[-1,1] #patterns <- rbind(rep(0,3),c(0,0,1),c(0,1,1),0:2) #4 hypothesis #gg <- fitGG(x,groups,patterns,method='EBayes') #gg #gg <- parest(gg,x,groups) #gg#Not run #library(EBarrays); data(gould) #x <- log(exprs(gould)[,-1]) #exclude 1st array #groups <- pData(gould)[-1,1] #patterns <- rbind(rep(0,3),c(0,0,1),c(0,1,1),0:2) #4 hypothesis #gg <- fitGG(x,groups,patterns,method='EBayes') #gg #gg <- parest(gg,x,groups) #gg
Produces plot to visualize simulated trajectories of the summary
statistic as a function of the number of additional data batches,
i.e. the output produced by forwsimDiffExpr.
plotForwSim(fs,xlab="Number of additional batches per group",ylab="Expected increase in True Positives",col='gray',lty=1,...)plotForwSim(fs,xlab="Number of additional batches per group",ylab="Expected increase in True Positives",col='gray',lty=1,...)
fs |
Forward simulation results, as output by |
xlab |
x-axis label |
ylab |
y-axis label |
col |
line color |
lty |
line type |
... |
Other arguments to be passed to |
Produces a plot.
David Rossell
Rossell D., Mueller P. Sequential sample sizes for high-throughput hypothesis testing experiments. http://sites.google.com/site/rosselldavid/home.
Computes posterior means for the gene expression levels using a GaGa or MiGaGa model.
posmeansGG(gg.fit, x, groups, sel, underpattern)posmeansGG(gg.fit, x, groups, sel, underpattern)
gg.fit |
GaGa or MiGaGa fit (object of type |
x |
|
groups |
If |
sel |
Numeric vector with the indexes of the genes we want to
draw new samples for (defaults to all genes). If a logical vector is
indicated, it is converted to |
underpattern |
Expression pattern assumed to be true (defaults to
last pattern in |
The posterior distribution of the mean parameters actually depends on
the gene-specific shape parameter(s), which is unknown. To speed up
computations, a gamma approximation to the shape parameter posterior
is used (see rcgamma for details) and the shape parameter is
fixed to its mode a posteriori.
Matrix with mean expression values a posteriori, for each selected gene and each group. Genes are in rows and groups in columns.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
fitGG for fitting GaGa and MiGaGa models,
parest for computing posterior probabilities of
each expression pattern.
Estimates posterior expected probability that a future sample is correctly classified when performing class prediction. The estimate is obtained via Monte Carlo simulation from the posterior predictive.
powclasspred(gg.fit, x, groups, prgroups, v0thre=1, ngene=100, B=100)powclasspred(gg.fit, x, groups, prgroups, v0thre=1, ngene=100, B=100)
gg.fit |
GaGa or MiGaGa fit (object of type |
x |
|
groups |
If |
prgroups |
Vector specifying prior probabilities for each group. Defaults to equally probable groups. |
v0thre |
Only genes with posterior probability of being equally
expressed below |
ngene |
Number of genes to use to build the classifier. Genes with smaller probability of being equally expressed are selected first. |
B |
Number of Monte Carlo samples to be used. |
The routine simulates future samples (microarrays) from the posterior
predictive distribution of a given group (e.g. control/cancer).
Then it computes the posterior probability
that the new sample belongs to each of the groups
and classifies the sample into the group with
highest probability. This process is repeated B times, and the
proportion of correctly classified samples is reported for each
group. The standard
error is obtained via the usual normal approximation (i.e. SD/B).
The overall probability of correct classification is also provided
(i.e. for all groups together), but using a more efficient variant of
the algorithm. Instead of reporting the observed proportion of
correctly classified samples, it reports the expected proportion of
correctly classified samples (i.e. the average posterior probability
of the class that the sample is assigned to).
List with components:
ccall |
Estimated expected probability of correctly classifying a future sample. |
seccall |
Estimated standard error of |
ccgroup |
Vector with the estimated probability of correctly classifying a sample from each group. |
segroup |
Estimated standard error of |
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
classpred, fitGG,
parest. See powfindgenes for differential
expression power calculations.
powfindgenes evaluates the posterior expected number of true positives
(e.g. true gene discoveries) if one were to obtain an additional batch
of data. It uses either a GaGa or a normal-normal model fit on a pilot
data set.
powfindgenes(fit, x, groups, batchSize = 1, fdrmax = 0.05, genelimit, v0thre = 1, B = 1000, mc.cores=1)powfindgenes(fit, x, groups, batchSize = 1, fdrmax = 0.05, genelimit, v0thre = 1, B = 1000, mc.cores=1)
fit |
GaGa/MiGaGa or normal-normal model fit using pilot data
|
x |
|
groups |
If |
batchSize |
Number of additional samples to obtain per group. |
fdrmax |
Upper bound on FDR. |
.
genelimit |
Only the |
v0thre |
Only genes with posterior probability of being equally
expressed < |
B |
Number of simulations from the GaGa predictive distribution to be used to estimate the posterior expected number of true positives. |
mc.cores |
If |
The routine simulates data from the posterior predictive distribution
of a GaGa or normal-normal model. That is, first it simulates parameter values (differential
expression status, mean expression levels etc.) from the posterior
distribution. Then it simulates data using the
parameter values drawn from the posterior.
Finally the simulated data is used to determine the differential status
of each gene, controlling the Bayesian FDR at the fdrmax level,
as implemented in findgenes.
As the differential expression status is known for each gene, one can
evaluate the number of true discoveries in the reported gene list.
In order to improve speed, hyper-parameters are not re-estimated when computing posterior probabilities for the posterior predictive simulated data.
m |
Posterior expected number of true positives (as estimated by
the sample mean of |
s |
Standard error of the estimate i.e. SD of the simulations/sqrt(B) |
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
findgenes, fitGG, fitNN,
parest. See powclasspred for
power calculations for sample classification.
#Simulate data and fit GaGa model set.seed(1) x <- simGG(n=20,m=2,p.de=.5,a0=3,nu=.5,balpha=.5,nualpha=25) gg1 <- fitGG(x,groups=1:2,method='EM') gg1 <- parest(gg1,x=x,groups=1:2) #Expected nb of TP for 1 more sample per group powfindgenes(gg1,x=x,groups=1:2,batchSize=1,fdrmax=.05)$m #Expected nb of TP for 10 more samples per group powfindgenes(gg1,x=x,groups=1:2,batchSize=10,fdrmax=.05)$m#Simulate data and fit GaGa model set.seed(1) x <- simGG(n=20,m=2,p.de=.5,a0=3,nu=.5,balpha=.5,nualpha=25) gg1 <- fitGG(x,groups=1:2,method='EM') gg1 <- parest(gg1,x=x,groups=1:2) #Expected nb of TP for 1 more sample per group powfindgenes(gg1,x=x,groups=1:2,batchSize=1,fdrmax=.05)$m #Expected nb of TP for 10 more samples per group powfindgenes(gg1,x=x,groups=1:2,batchSize=10,fdrmax=.05)$m
Prints an object of class gagaclus, which contains the result
of clustering genes into expression patterns.
## S3 method for class 'gagaclus' print(x, ...)## S3 method for class 'gagaclus' print(x, ...)
x |
Object of type |
... |
Other arguments to be passed on to the generic print function. |
Displays the expression patterns and the number of genes classified into each of them.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
Prints an object of class gagafit (as returned by fitGG
or parest) or class nnfit. Provides general information and hyper-parameter
estimates, if available.
## S3 method for class 'gagafit' print(x,...) ## S3 method for class 'nnfit' print(x,...)## S3 method for class 'gagafit' print(x,...) ## S3 method for class 'nnfit' print(x,...)
x |
Object of type |
... |
Other arguments to be passed on to the generic print function. |
fitGG does not create a complete gagafit object. The
complete object is returned by parest, which computes the
posterior probabilities of differential expression and obtain
hyper-parameter estimates (these are only provided by fitGG
when the option method='EBayes' is used).
Prints number of genes, hypotheses, details about the model fitting and hyper-parameter estimates (when available).
David Rossell
Prints an object of class gagahyp, which contains information
on the hypotheses (expression patterns) from a GaGa or MiGaGa model.
## S3 method for class 'gagahyp' print(x, probpat=NA, ...)## S3 method for class 'gagahyp' print(x, probpat=NA, ...)
x |
Object of type |
probpat |
Vector with either estimated probabilities of each hypothesis, or with number of genes classified into each expression pattern. |
... |
Other arguments to be passed on to the generic print function. |
Prints hypotheses. When available, also displays estimated proportion of genes following each expression pattern or the number of genes classified into each expression pattern.
David Rossell
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. http://rosselldavid.googlepages.com.
Estimate the expected utility for sequential boundaries
parameterized by (b0,b1). Expected utility is estimated on a grid of
(b0,b1) values based on a forward simulation output such as that
generated by the function forwsimDiffExpr.
seqBoundariesGrid(b0, b1, forwsim, samplingCost, powmin = 0, f = "linear", ineq = "less")seqBoundariesGrid(b0, b1, forwsim, samplingCost, powmin = 0, f = "linear", ineq = "less")
b0 |
Vector with b0 values. Expected utility is evaluated for a grid defined by all combinations of (b0,b1) values. |
b1 |
Vector with b1 values. |
forwsim |
|
samplingCost |
Cost of obtaining one more data batch, in terms of the number of new truly differentially expressed discoveries that would make it worthwhile to obtain one more data batch. |
powmin |
Constraint on power. Optimization chooses the optimal
|
f |
Parametric form for the stopping boundary. Currently only
'linear' and 'invsqrt' are implemented. For 'linear', the boundary
is |
ineq |
For |
Intuitively, the goal is to stop collecting new data when the expected
benefit of obtaining one more data batch is small, i.e. below a
certain boundary. We consider two simple parametric forms for such a
boundary (linear and inverse square root), which allows to easily evaluate
the expected utility for each boundary within a grid of parameter
values.
The optimal boundary is defined by the parameter values achieving the
largest expected utility, restricted to parameter values with an
estimated power greater or equal than powmin.
Here power is defined as the expected number of true discoveries
divided by the expected number of differentially expressed entities.
The routine evaluates the expected utility, as well as expected FDR, FNR, power and sample size for each specified boundary, and also reports the optimal boundary.
A list with two components:
opt |
Vector with optimal stopping boundary ( |
grid |
|
David Rossell.
Rossell D., Mueller P. Sequential sample sizes for high-throughput hypothesis testing experiments. http://sites.google.com/site/rosselldavid/home.
Rossell D. GaGa: a simple and flexible hierarchical model for microarray data analysis. Annals of Applied Statistics, 2009, 3, 1035-1051.
simGG simulates parameters and data from the prior-predictive of GaGa/ MiGaGa models with several groups, fixing the hyper-parameters.
simLNN simulates from a log-normal normal with gene-specific variances (LNNMV in package EBarrays). simNN returns the log observations.
simGG(n, m, p.de=.1, a0, nu, balpha, nualpha, equalcv = TRUE, probclus = 1, a = NA, l = NA, useal = FALSE) simLNN(n, m, p.de=0.1, mu0, tau0, v0, sigma0) simNN(n, m, p.de=0.1, mu0, tau0, v0, sigma0)simGG(n, m, p.de=.1, a0, nu, balpha, nualpha, equalcv = TRUE, probclus = 1, a = NA, l = NA, useal = FALSE) simLNN(n, m, p.de=0.1, mu0, tau0, v0, sigma0) simNN(n, m, p.de=0.1, mu0, tau0, v0, sigma0)
n |
Number of genes. |
m |
Vector indicating number of observations to be simulated for each group. |
p.de |
Probability that a gene is differentially expressed. |
a0, nu
|
Mean expression for each gene is generated from
|
balpha, nualpha
|
Shape parameter for each gene is generated
from |
equalcv |
If |
probclus |
Vector with the probability of each component in the mixture. Set to 1 for the GaGa model. |
a, l
|
Optionally, if |
useal |
For |
mu0, tau0
|
Gene-specific means arise from N(mu0,tau0^2) |
v0, sigma0
|
Gene-specific variances arise from IG(.5*nu0,.5*nu0*sigma0^2) |
For the GaGa model, the shape parameters are actually drawn from a gamma approximation to
their posterior distribution. The function rcgamma implements
this approximation.
Object of class 'ExpressionSet'. Expression values can be accessed via
exprs(object) and the parameter values used to generate the
expression values can be accessed via fData(object).
Currently, the routine only implements prior predictive simulation for the 2 hypothesis case.
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.
simnewsamples to simulate from the posterior
predictive, checkfit for graphical posterior predictive checks.
#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) # #plot(density(xsim$x),main='') #plot(xsim$l,xsim$a,ylab='Shape',xlab='Mean')#Not run. Example from the help manual #library(gaga) #set.seed(10) #n <- 100; m <- c(6,6) #a0 <- 25.5; nu <- 0.109 #balpha <- 1.183; nualpha <- 1683 #probpat <- c(.95,.05) #xsim <- simGG(n,m,p.de=probpat[2],a0,nu,balpha,nualpha) # #plot(density(xsim$x),main='') #plot(xsim$l,xsim$a,ylab='Shape',xlab='Mean')
Posterior and posterior predictive simulation for GaGa/MiGaGa and Normal-Normal models.
simnewsamples(fit, groupsnew, sel, x, groups)simnewsamples(fit, groupsnew, sel, x, groups)
fit |
Either GaGa or MiGaGa fit (object of type |
groupsnew |
Vector indicating the group that each new sample
should belong to. |
sel |
Numeric vector with the indexes of the genes we want to
draw new samples for (defaults to all genes). If a logical vector is
indicated, it is converted to |
x |
|
groups |
If |
For GaGa/MiGaGa models, the shape parameters are actually drawn from a gamma approximation to
their posterior distribution. The function rcgamma implements
this approximation.
In order to be consistent with the LNNGV model implemented in emfit (package EBarrays), for the Normal-Normal model the variance is drawn from an inverse gamma approximation to its marginal posterior (obtained by plugging in the group means, see EBarrays vignette for details).
Object of class 'ExpressionSet'. Expression values can be accessed via
exprs(object) and the parameter values used to generate the
expression values can be accessed via fData(object).
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.
checkfit for posterior predictive plot,
simGG for prior predictive simulation.