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]