pcNEM: Probabilistic combinatorial nested effects model

Description Usage Arguments Details Value Author(s) References Examples

Allows to set and retrieve various hyperparameters for different inference methods.

1	set.default.parameters(Sgenes, ...)

`Sgenes`	character vector of S-gene identifiers
`...`	parameters to set (see details)

Since version 2.5.4 functions in the nem package do not have any more a large amount of individual parameters. Instead there is just one hyperparameter, which is passed to all functions. Parameter values with the hyperparameter can be set with this function.

type: mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP or depn. CONTmLLDens and CONTmLLRatio are identical to CONTmLLBayes and CONTmLLMAP and are still supported for compatibility reasons. mLL and FULLmLL are used for binary data (see BoutrosRNAiDiscrete) and CONTmLL for a matrix of effect probabilities. CONTmLLBayes and CONTmLLMAP are used, if log-odds ratios, p-value densities or any other model specifies effect likelihoods. CONTmLLBayes refers to an inference scheme, were the linking positions of effect reporters to network nodes are integrated out, and CONTmLLMAP to an inference scheme, were a MAP estimate for the linking positions is calculated. depn indicates Deterministic Effects Propagation Networks (DEPNs).
para: vector of length two: false positive rate and false negative rate for binary data. Used by mLL
hyperpara: vector of length four: used by FULLmLL() for binary data
Pe: prior of effect reporter positions in the phenotypic hierarchy (same dimension as D). Not used type depn. Default: NULL
Pm: prior over models (n x n matrix). Default: NULL
Pm.frac_edges: expected fraction of edges in the true S-gene graph
Pmlocal: local model prior for pairwise and triple learning. For pairwise learning generated by local.model.prior according to arguments local.prior.size and local.prior.bias
local.prior.size: prior expected number of edges in the graph (for pairwise learning). Default: no. nodes
local.prior.bias: bias towards double-headed edges. Default: 1 (no bias; for pairwise learning)
triples.thrsh: threshold for model averaging to combine triple models for each edge. Default: 0.5
lambda: regularization parameter to incorporate prior assumptions. May also be a vector of possible values, if nemModelSelection is used, Default: 0 (no regularization)
delta: regularization parameter for automated subset selection of effect reporters. If no E-gene selection is wanted, set delta to 0. Default: 1/ (no. S-genes + 1)
selEGenes.method: If "regularization", E-gene selection is performed by introducing a "null" S-gene to which E-genes are attached with probability delta/ (no. S-genes + 1). If "iterative" and selEGenes=TRUE, getRelevantEGenes is called and a new model is trained on the selected E-genes. The process is then repeated until convergence. Default: "regularization"
selEGenes: Tune parameter delta for automated selection of E-genes. Default: FALSE. NOTE: Since version > 2.18.0 E-gene selection is now performed per default by using the regularization mechanism with parameter delta. If no E-gene selection is wanted, set delta to 0.
trans.close: Should always transitive closed graphs be computed? Default: TRUE. NOTE: This has only an impact for type nem.greedyMAP and depn. Default: TRUE
backward.elimination: For module networks and greedy hillclimbing inference: Try to eliminate edges increasing the likelihood. Works only, if trans.close=FALSE. Default: FALSE
mode: For Bayesian network inference and DEPNs: binary_ML: effects come from a binomial distribution - ML learning of parameters (Bayesian networks only); binary_Bayesian: effects come from a binomial distribution - Bayesian learning of parameters (Bayesian networks only); continuous_ML: effects come from a normal distribution - ML learning of parameters; continuous_Bayesian: effects come from a normal distribution - Bayesian learning of parameters.
nu.intervention, lambda.intervention: For depn: For any perturbed node we suppose the unknown mean mu given its unknown variance sigma2 to be drawn from N(nu.intervention, sigma2/lambda.intervention). Default: nu.intervention=0.6, lambda.intervention=4
nu.no\_intervention, lambda.no\_intervention: The same parameters for unperturbed nodes. Default: nu.no\_intervention=0.95, lambda.no\_intervention=4
df.intervention, scale.intervention: For depn: The unknown variance sigma2 for perturbed nodes is supposed to be drawn from Inv-χ^2(df.intervention, scale.intervention). Default: df.intervention=4.4, scale.intervention=4.4
df.no\_intervention, scale.no\_intervention: The same parameters for unperturbed nodes. Default: df.no\_intervention=4.4, scale.no\_intervention=0.023
map: For depn: Mapping of interventions to network nodes. The format is a named list of strings with names being the interventions and entries being the network nodes. In pc-NEM, this is called the perturbation map and is a matrix with interventions as rows and network nodes as columns. This matrix is a must for pc-NEM and each entry can take a value between 0 and 1. Default: For depn, entries and names are the network nodes.
outputdir: Directory where to put diagnostic plots. Default: folder "QualityControl" in current working directory
debug: Print out or plot diagnostic information. Default: FALSE
mc.cores: number of cores to be used on a multicore processor. Default: 8
mcmc.nsamples: Number of MCMC samples to take. Default: 1e6
mcmc.nburnin: Number of additional samples for burnin phase. Default: 1e6
mcmc.seed: random seed. Default: 1234
mcmc.hyperprior: Parameter for exponential distribution hyperprior for regularization parameter 1/lambda. Default: 1
eminem.maxsteps: Maximum number of iterations for the EM algorithm (MC.EMINEM). Default: 1000
eminem.sdVal: positive number, between 1 and ncol(D)*(ncol(D)-1): number of edges to change in one MCMC step; see paper for the author's choice. Default: 1
eminem.changeHfreq: positive number, mcmc.nsamples must be a multiple: the Empirical Bayes step is performed every <changeHfreq> steps (see paper for the author's choice); set >= mcmc.nsamples (or leave to default) to exclude this step. Default: NULL
prob.cutoff: Only edges with probability > prob.cutoff are assumed to be present. Default: 0.5
pcombi: Runs pc-NEM if set to TRUE and classical NEM if set to FALSE . Default: FALSE
iterations: Number of iterations in adaptive simulated annealing. Default: 2e4
stepsave: Adapting intervals in ASA. Default: 100
revallowed: Binary variable with 1 for allowing reversal of edges in ASA. Default: 1
AcceptRate: Ideal acceptance rate for ASA. Default: NULL
Temp: Initial temperature for ASA. Default: 50
AdaptRate: Adaptation rate for ASA. Default: 0.3
noiseEst: Estimates noise parameters when set to TRUE in ASA. Default: TRUE
moveprobs: The probabilities to switch between the two spaces of DAGs and error rates in ASA. Default: c(0.6,0.4)
moveprobsNoise: The probabilities to alternate between the two error rate spaces in ASA. Default: c(0.5,0.5)
sigma: Covariance matrix for the two error rates in ASA. Default: diag(x=1,2)/10)

A list containing all parameters described above.

Holger Froehlich

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., 2012, submitted.

Srivatsa S, Kuipers J, Schmich F, Eicher S, Emmenlauer M, Dehio C, Beerenwinkel N, Improved pathway reconstruction from RNA interference screens by exploiting off-target effects, ISMB, 2018

1	control = set.default.parameters(LETTERS[1:5], type="CONTmLLBayes", selEGenes=TRUE) # set inference type and whether to use automatic E-gene selection for a network with nodes "A"-"E".