nem {nem}R Documentation

Nested Effects Models - main function

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=TRUE)

## 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, nem.greedy for greedy hillclimbing, nem.greedyMAP for alternating MAP optimization using log odds or log p-value densities
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 and parameter type == CONTmLLMAP, the regularization parameter delta is tuned via the AIC model selection criterion. Otherwise, an iterative algorithm is executed, which in an alternating optimization scheme reconstructs a network given the current set of effect reporters and then selects the effect reporters having the highest likelihood under the given network. The procedure is run until convergence.

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 the inferred directed graph (graphNEL object)
mLL log posterior marginal likelihood of final model
pos posterior over effect positions
mappos MAP estimate of effect positions
selected selected E-gene subset
LLperGene likelihood per selected E-gene
control hyperparameter as in function call

Author(s)

Holger Froehlich <URL: http:/www.dkfz.de/mga2/people/froehlich>, Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

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")

[Package nem version 2.8.0 Index]