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` | see `nem`
`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

nem object

### 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. Bioinformatics, 1, 2008.

### See Also

`score`, `nem`

### Examples

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

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

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

       # estimate of effect positions
       res$mappos

[Package nem version 2.6.0 Index]