nem.greedyMAP {nem}R Documentation

Infers a phenotypic hierarchy using an alternating MAP optimization

Description

Starting with an initial estimate of the linking of E-genes to S-genes from the data, this method performs an alternating MAP optimization of the S-genes graph and the linking graph until convergence. As a final step the function closest.transitive.greedy can be invoked to find a transitively closed graph most similar to the original one.

Usage

nem.greedyMAP(D,Pe=NULL,Pm=NULL,lambda=0,delta=1, trans.close=TRUE, verbose=TRUE)

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

Arguments

D data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
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
trans.close find a similar transitively closed graph
verbose do you want to see progress statements printed or not? Default: TRUE
x nem object
... other arguments to pass

Value

nem object

Author(s)

Holger Froehlich

See Also

nem, closest.transitive.greedy

Examples

   data("BoutrosRNAi2002") 
   res <- nem(BoutrosRNAiLods, inference="nem.greedyMAP", delta=0)
   
   # 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]