birteRun: Main interface for Bayesian Inference of Regulatory Influence on Expression (biRte).

Description

The function estimates regulator activities via MCMC sampling. The function assumes experimental data of two conditions to be given. If desired, influences on mRNA log fold changes can be estimated rather than on expression levels itself.

Besides miRNA, mRNA and TF expression data, biRte also allows to integrate other data types (e.g. CNV data - data type 'other').

birteLimma is a convenience function, which allows to directly pass results from a previous limma analysis (see limmaAnalysis) to birteRun. When working with relative expression levels (log fold changes) birteLimma allows to deal with arbitrary complex statistical designs, including e.g. time as a covariate.

Usage

birteRun(dat.mRNA, mRNA.Sigma=NULL, nrep.mRNA=c(5, 5),  df.mRNA=sum(nrep.mRNA)-2, data.regulators=NULL, sigma.regulators=NULL,  nrep.regulators=NULL, diff.regulators=NULL,  	init.regulators=NULL, theta.regulators=NULL,  reg.interactions=FALSE, affinities, use.affinities=FALSE,     niter=100000, nburnin=100000, thin=50,  potential_swaps=NULL, only_switches=FALSE, only.diff.TFs=TRUE,  explain.LFC=TRUE, model=c("no-plug-in", "all-plug-in")) 
birteLimma(dat.mRNA, limmamRNA, 	data.regulators=NULL, limma.regulators=NULL,  fdr.regulators=NULL, lfc.regulators=NULL,
		init.regulators=NULL, theta.regulators=NULL,  reg.interactions=FALSE, affinities, use.affinities=FALSE,
	  niter=100000, nburnin=100000, thin=50,  potential_swaps=NULL, only_switches=FALSE, only.diff.TFs=TRUE,  explain.LFC=TRUE, model=c("no-plug-in", "all-plug-in"))

Arguments

dat.mRNA

mRNA expression data matrix. IMPORTANT: Replicates must be ordered according to nrep.mRNA

mRNA.Sigma

gene expression variances (array data). IMPORTANT: Names have to match the row names in dat.mRNA. If mRNA.Sigma = NULL, birte.run tries to deduce variances from a limma analysis (see limmaAnalysis)

nrep.mRNA

number of replicates per condition.

df.mRNA

residual degrees of freedom of linear model for mRNA data.

data.regulators

list with at most 3 components (miRNA, TF, other). Each component contains one data matrix. IMPORTANT: Samples in data matrices have to be grouped according to conditions. That means first there are all samples from the first condition, then those from the second condition, etc.

sigma.regulators

list with at most 3 components (miRNA, TF, other). Each component contains one named vector of expression variances (array data) or dispersion parameters (RNAseq data). IMPORTANT: Names have to fit to the row names of the data matrix.

nrep.regulators

list with at most 3 components (miRNA, TF, other). Each component contains the number of replicates per condition

diff.regulators

list with at most 3 components (miRNA, TF, other). Each component is a character vector with differentially expressed regulators. Has to be subset of row names of the data matrix

init.regulators

list with at most 3 components (miRNA, TF, other). Each component is matrix of #conditions x length(affinities[[regulator type]]): initial states for regulators. In case this matrix is not provided (i.e. NULL) initial states are assumed to be 0. IMPORTANT: column names have to match names(affinities[[regulator type]])

theta.regulators

list with at most 3 components (miRNA, TF, other). If single numbers are provided, each component contains the expected fraction of active regulators. If vectors are provided, each vector entry corresponds to the individual probability of a specific regulator to be active. Accordingly, vectors should be named in agreement with the regulator-target gene network. If affinities$other corresponds to interaction terms between regulators, theta.regulators can also be provided as a #regulators x #regulators matrix.

reg.interactions

If TRUE, entries of affinities$other are interpreted as interaction terms between regulators.

affinities

Regulator-target gene interactions. This is a list with at most three components (TF, miRNA, other). Each of these lists again contains a weighted adjacency list representation. See humanNetworkSimul for an example. IMPORTANT: gene names used in this network have to match with row names of dat.mRNA. Moreover, regulator names have to fit to row names of the corresponding data matrices.

use.affinities

Should weights given in the bipartite regulator-target gene graph given a specific meaning? If yes, it is assumed that weights correspond to quantitative influences of regulators on their targets.

niter

Number of MCMC iterations (AFTER burnin).

nburnin

Number of MCMC iterations UNTIL burnin is assumed to be finished.

thin

Thinning of Markov chain: only use every thin's sample for posterior computation.

potential_swaps

Pre-computed potential swaps (OPTIONAL, see get_potential_swaps).

only_switches

Should only switches be performed?

only.diff.TFs

Should, in case of TF expression data, only the information for differentially expressed TFs be considered? Note that this makes fewer assumption about the relation of mRNA and protein expression data, but typically leads to less conservative results (i.e. more TFs predicted to be active).

limmamRNA

results of limma analysis for mRNA data according to limmaAnalysis

limma.regulators

list with at most 3 components (miRNA, TF, other). Each component contains the results of a limma analysis for regulator data according to limmaAnalysis

lfc.regulators

list with at most 3 components (miRNA, TF, other). Each component contains the log fold change cutoff for differential expression. It is assumed to be 0, if not provided.

fdr.regulators

list with at most 3 components (miRNA, TF, other). Each component contains the FDR cutoff for differential expression (DEFAULT: 0.05).

explain.LFC

If yes, biRte tries to explain mRNA log fold changes rather than expression levels itself.

model

If "no-plug-in", for marginal log likelihoods are considered for regulator specific expression data. Otherwise, (posterior) variance estimates are used directly.

Value

post: #regulators x #conditions matrix containing the marginal probability for each regulator to influence mRNA expression.
map: #regulators x #conditions matrix containing the regulator configuration with highest joint probability.
coef: matrix of #conditions x #replicates: Each entry is itself a matrix (embedded into a list) of #coefficients x #effective samples. The data contains the expected regression coefficients.
log_lik_trace: (Marginal) log-likelihood trace of MCMC sampling.
eff_sample_size: effective sample size after burnin and thinning
contains.interactions: TRUE, if affinities$other corresponds to interaction terms between regulators, FALSE otherwise
explain.LFC: TRUE, if model explains mRNA log fold change, FALSE otherwise
nburnin: number of burnin iterations - as provided as an argument
affinities: original regulator-target gene network
C_cnt: number of conditions
design: design matrix effectively used for model training
param: estimated parameters for mRNA precision (i.e. inverse variance) distribution

Examples

Run this code

# artificial data
data(humanNetworkSimul)
sim = simulateData(affinities2)
limmamRNA = limmaAnalysis(sim$dat.mRNA, design=NULL, "treated - control")
limmamiRNA = limmaAnalysis(sim$dat.miRNA, design=NULL, "treated - control")
limmaTF = limmaAnalysis(sim$dat.TF, design=NULL, "treated - control")

# burnin and sampling size is much too small in reality
result = birteLimma(dat.mRNA=sim$dat.mRNA, 
data.regulators=list(miRNA=sim$dat.miRNA, TF=sim$dat.TF), 
limmamRNA=limmamRNA, limma.regulators=list(miRNA=limmamiRNA, TF=limmaTF), 
affinities=affinities2, niter=100, nburnin=100, thin=2) 
plotConvergence(result)
pred = birtePredict(result, rownames(sim$dat.mRNA))
MSE.Bayes = mean((pred[[1]][[1]]$mean - limmamRNA$pvalue.tab[rownames(sim$dat.mRNA),"logFC"])^2)
MSE.Bayes


# real data
library(Biobase)
data(EColiOxygen)
# prepare network
affinities = list(TF=sapply(names(EColiNetwork$TF), function(tf){
w = rep(1, length(EColiNetwork$TF[[tf]])); 
names(w)= EColiNetwork$TF[[tf]]; w}))
# prepare data
colnames(exprs(EColiOxygen)) = make.names(paste(pData(EColiOxygen)$GenotypeVariation, 
pData(EColiOxygen)$GrowthProtocol, sep="."))
mydat = cbind(exprs(EColiOxygen)[,colnames(exprs(EColiOxygen)) =="wild.type.aerobic"], 
exprs(EColiOxygen)[,colnames(exprs(EColiOxygen)) == "wild.type.anaerobic"])

# more realistic sampling
## Not run: 
# result = birteRun(dat.mRNA=mydat, 
# nrep.mRNA=c(3,4), affinities=affinities, niter=10000, nburnin=10000)
# plotConvergence(result)
# ## End(Not run)

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