nem: Nested Effects Models - main function

In nem: (Dynamic) Nested Effects Models and Deterministic Effects Propagation Networks to reconstruct phenotypic hierarchies

Description

The main function to perform model learning from data

Usage

nem(D,inference="nem.greedy",models=NULL,control=set.default.parameters(setdiff(unique(colnames(D)),"time")), verbose=FALSE)

## S3 method for class 'nem'
print(x, ...)

Arguments

D	data matrix with experiments in the columns (binary or continious)
inference	search to use exhaustive enumeration, triples for triple-based inference, pairwise for the pairwise heuristic, ModuleNetwork for the module based inference proposed in Fr\"ohlich et al. 2008, ModuleNetwork.orig for the module based inference proposed in Fr\"ohlich et al. 2007, nem.greedy for greedy hillclimbing, nem.greedyMAP for alternating MAP optimization using log odds or log p-value densities, mc.eminem for EM based inference using log odds or log p-value densities, BN.greedy, BN.exhaustive for a conventional Bayesian Network treatment using binomial or normal distribution assumptions, dynoNEM for MCMC based inference from time series data, mc.eminem for EM based inference
models	a list of adjacency matrices for model search. If NULL, an exhaustive enumeration of all possible models is performed.
control	list of parameters: see set.default.parameters
verbose	do you want to see progression statements? Default: TRUE
x	nem object
...	other arguments to pass

Details

If parameter Pm != NULL and parameter lambda == 0, a Bayesian approach to include prior knowledge is used. Alternatively, the regularization parameter lambda can be tuned in a model selection step via the function nemModelSelection using the BIC criterion. If automated subset selection of effect reporters is used (default), the regularization parameter delta can be tuned via the BIC model selection criterion. Per default it is fixed to 1 / (no. S-genes + 1).

The function plot.nem plots the inferred phenotypic hierarchy as a directed graph, the likelihood distribution of the models (only for exhaustive search) or the posterior position of the effected genes.

Value

graph	inferred directed S-gene graph (graphNEL object)
mLL	log posterior marginal likelihood of model(s)
pos	posterior over effect positions
mappos	MAP estimate of effect positions
selected	selected E-gene subset
LLperGene	likelihood per selected E-gene
avg	in case of MCMC: posterior mean S-gene graph (edge weighted adjacency matrix)
control	hyperparameter as in function call

For inference = "mc.eminem" the following additional values are returned:

local.maxima	local maxima of the EM procedure
graphs.sampled	sampled graphs
EB	samples of the empirical Bayes prior
acc_list	list that indicates whether the corresponding sampled S-gene graph has been accepted (new local maximum (1), same local maximum (0)) or rejected(-1) in the MCMC sampling process - length(acc_list)=mcmc.nsamples + mcmc.nburnin

Author(s)

Holger Froehlich, Florian Markowetz

References

Markowetz, F.; Bloch, J. & Spang, R., Non-transcriptional Pathway Features Reconstructed from Secondary Effects of RNA interference. Bioinformatics, 2005, 21, 4026 - 4032\

Markowetz, F.; Kostka, D.; Troyanskaya, O. & Spang, R., Nested Effects Models for High-dimensional Phenotyping Screens. Bioinformatics, 2007, 23, i305 - i312\

Fr\"ohlich, H.; Fellmann, M.; S\"ultmann, H.; Poustka, A. & Beissbarth, T. Large Scale Statistical Inference of Signaling Pathways from RNAi and Microarray Data. BMC Bioinformatics, 2007, 8, 386\

Fr\"ohlich, H.; Fellmann, M.; S\"ultmann, H.; Poustka, A. & Beissbarth, T. Estimating Large Scale Signaling Networks through Nested Effect Models with Intervention Effects from Microarray Data. Bioinformatics, 2008, 24, 2650-2656\

Tresch, A. & Markowetz, F., Structure Learning in Nested Effects Models Statistical Applications in Genetics and Molecular Biology, 2008, 7\

Zeller, C.; Fr\"ohlich, H. & Tresch, A., A Bayesian Network View on Nested Effects Models EURASIP Journal on Bioinformatics and Systems Biology, 2009, 195272\

Fr\"ohlich, H.; Tresch, A. & Beissbarth, T., Nested Effects Models for Learning Signaling Networks from Perturbation Data. Biometrical Journal, 2009, 2, 304 - 323\

Fr\"ohlich, H.; Sahin, \"O.; Arlt, D.; Bender, C. & Beissbarth, T. Deterministic Effects Propagation Networks for Reconstructing Protein Signaling Networks from Multiple Interventions. BMC Bioinformatics, 2009, 10, 322\

Fr\"ohlich, H.; Praveen, P. & Tresch, A., Fast and Efficient Dynamic Nested Effects Models. Bioinformatics, 2011, 27, 238-244\

Niederberger, T.; Etzold, S.; Lidschreiber, M; Maier, K.; Martin, D.; Fr\"ohlich, H.; Cramer, P.; Tresch, A., MC Eminem Maps the Interaction Landscape of the Mediator, PLoS Comp. Biol., 8(6): e1002568, 2012.

See Also

set.default.parameters, nemModelSelection, nem.jackknife, nem.bootstrap, nem.consensus, local.model.prior, plot.nem

Examples

   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]
   control = set.default.parameters(unique(colnames(D)), para=c(0.13, 0.05))   
   res1 <- nem(D,inference="search", control=control)
   res2 <- nem(D,inference="pairwise", control=control)
   res3 <- nem(D,inference="triples", control=control)
   res4 <- nem(D,inference="ModuleNetwork", control=control)
   res5 <- nem(D,inference="nem.greedy", control=control)        
   res6 = nem(BoutrosRNAiLods, inference="nem.greedyMAP", control=control)
   

   par(mfrow=c(2,3))
   plot.nem(res1,main="exhaustive search")
   plot.nem(res2,main="pairs")
   plot.nem(res3,main="triples")
   plot.nem(res4,main="module network")
   plot.nem(res5,main="greedy hillclimber")      
   plot.nem(res6,main="alternating MAP optimization")

nem documentation built on Oct. 31, 2019, 2:12 a.m.