moduleNetwork {nem} | R Documentation

## Infers a phenotypic hierarchy using the module network

### Description

Function `moduleNetwork` estimates the hierarchy using a divide and conquer approach. In each step only a subset of nodes (called module) is involved and no exhaustive enumeration of model space is needed as in function `score`.

### Usage

    moduleNetwork(D,type="mLL",Pe=NULL,Pm=NULL,lambda=0,delta=1,para=NULL,hyperpara=NULL,verbose=TRUE)

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

### Arguments

`D` | data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
`type` | (1.) marginal likelihood "mLL" (only for cout matrix D), or (2.) full marginal likelihood "FULLmLL" integrated over a and b and depending on hyperparameters a0, a1, b0, b1 (only for count matrix D), or (3.) "CONTmLL" marginal likelihood for probability matrices, or (4.) "CONTmLLDens" marginal likelihood for probability density matrices, or (5.) "CONTmLLRatio" for log-odds ratio matrices
`Pe` | prior position of effect reporters. Default: uniform over nodes in hierarchy
`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)
`para` | vector with parameters a and b for "mLL", if count matrices are used
`hyperpara` | vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"
`verbose` | do you want to see progress statements printed or not? Default: TRUE
`x` | nem object
`...` | other arguments to pass

### Details

`moduleNetwork` is an alternative to exhaustive search by the function `score` and more accurate than `pairwise.posterior` and `triples.posterior`. It uses clustering to sucessively split the network into smaller modules, which can then be estimated completely. Connections between modules are estimated by performing a constraint greedy hillclimbing.

### Value

`graph` | the inferred directed graph (graphNEL object)
`mLL` | marginal likelihood of final model
`pos` | posterior over effect positions
`mappos` | MAP estimate of effect positions
`type` | as used in function call
`para` | as used in function call
`hyperpara` | as used in function call
`lambda` | as in function call
`selected` | selected E-gene subset

### Author(s)

Holger Froehlich

### References

[1]
  Froehlich H, Fellmann M, Sueltmann H, Poustka A, Beissbarth T: Large Scale Statistical Inference of Signaling Pathways from RNAi and Microarray Data. BMC Bioinformatics, 2007.
[2]
  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. Proc. German Conf. Bioinformatics (GCB), pp. 45 - 54, 2007.

### See Also

`score`, `nem`

### Examples

       data("BoutrosRNAi2002")
       res <- moduleNetwork(BoutrosRNAiDiscrete[,9:16],para=c(.13,.05))

       # plot graph
       plot(res,what="graph")

       # plot posterior over effect positions
       plot(res,what="pos")

       # estimate of effect positions
       res$mappos

[Package nem version 2.4.0 Index]