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]