score {nem}R Documentation

Computes the marginal likelihood of phenotypic hierarchies

Description

Function to compute the marginal likelihood of a set of phenotypic hierarchies.

Usage

score(models, D, type="mLL", para=NULL, hyperpara=NULL, Pe=NULL, Pm=NULL, lambda=0, delta=1, verbose=TRUE, graphClass="graphNEL")

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

PhiDistr(Phi, Pm, a=1, b=0.5)

Arguments

models a list of adjacency matrices with unit main diagonal
D data matrix. Columns correspond to the nodes in the silencing scheme. Rows are effect reporters.
type mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP. 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 E-genes to S-Genes are integrated out, and CONTmLLMAP to an inference scheme, were a MAP estimate for the linking positions is calculated.
para Vector with parameters a and b (for "mLL" with count data)
hyperpara Vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"
Pe prior position of effect reporters. Default: uniform over nodes in silencing scheme
Pm prior on model graph (n x n matrix) with entries 0 <= priorPhi[i,j] <= 1 describing the probability of an edge between gene i and gene j.
lambda regularization parameter to incorporate prior assumptions.
delta regularization parameter for automated E-gene subset selection (CONTmLLRatio only)
verbose output while running or not
graphClass output inferred graph either as graphNEL or matrix
x nem object
... other arguments to pass
Phi adjacency matrix
a parameter of the inverse gamma prior for v=1/lambda
b parameter of the inverse gamma prior for v=1/lambda

Details

Scoring models by marginal log-likelihood is implemented in function score. Input consists of models and data, the type of the score ("mLL", "FULLmLL", "CONTmLL" or "CONTmLLBayes" or "CONTmLLMAP"), the corresponding paramters (para) or hyperparameters (hyperpara), a prior for phenotype positions (Pe) and model structures Pm with regularization parameter lambda. If a structure prior Pm is provided, but no regularization parameter lambda, Bayesian model averaging with an inverse gamma prior on 1/lambda is performed. With type "CONTmLLMAP" usually an automated selection of most relevant E-genes is performed by introducing a "null" S-gene. The corresponding prior probability of leaving out an E-gene is set to delta/no. S-genes.

score is usually called within function nem.

Value

nem object

Author(s)

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

References

[1]
Markowetz F, Bloch J, Spang R, Non-transcriptional pathway features reconstructed from secondary effects of RNA interference, Bioinformatics, 2005.
[2]
Markowetz F, Probabilistic Models for Gene Silencing Data, PhD thesis, Free University Berlin, 2006.
[3]
Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T: Estimating Large Scale Signaling Networks through Nested Effects Models from Intervention Effects in Microarray Data. Bioinformatics, 1, 2008.
[4]
Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T: Large Scale Statistical Inference of Signaling Pathways from RNAi and Microarray Data, BMC Bioinformatics, 8:386, 2007.

See Also

nem, mLL, FULLmLL, enumerate.models

Examples

   # Drosophila RNAi and Microarray Data from Boutros et al, 2002
   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]

   # enumerate all possible models for 4 genes
   models <- enumerate.models(unique(colnames(D)))

   # score models with marginal likelihood
   result <- score(models,D,type="mLL",para=c(.13,.05))
   
   # plot graph of the best model
   plot(result,what="graph")

   # plot scores
   plot(result,what="mLL") 
    
   # plot posterior of E-gene positions according to best model
   plot(result,what="pos")
   
   # MAP estimate of effect positions for the best model
   result$mappos
[Package nem version 2.6.0 Index]