set.default.parameters {nem} | R Documentation

## Get/set hyperparameters

### Description

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

### Usage

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

### Arguments

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

### 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. Default: 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

### Value

A list containing all parameters described above.

### Author(s)

Holger Froehlich

### 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., 2012, submitted.

### Examples

    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".

[Package nem version 2.56.0 Index]