pairwise.posterior {nem}R Documentation

Infers a phenotypic hierarchy edge by edge

Description

Function pairwise.posterior estimates the hierarchy edge by edge. In each step only a pair of nodes is involved and no exhaustive enumeration of model space is needed as in function score.

Usage

pairwise.posterior(D, type = "mLL", para = NULL, hyperpara = NULL, 
    Pe = NULL, Pmlocal = NULL, Pm = NULL, lambda = 0, delta=1, verbose = TRUE)

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

Arguments

D data matrix. Columns correspond to the nodes in the silencing scheme. Rows are phenotypes.
type see nem
para vector with parameters a and b for "mLL", if count matrices are used
hyperpara vector with hyperparameters a0, b0, a1, b1 for "FULLmLL"
Pe prior position of effect reporters. Default: uniform over nodes in hierarchy
Pmlocal local model prior for the four models tested at each node: a vector of length 4 with positive entries summing to one
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 do you want to see progress statements printed or not? Default: TRUE
x nem object
... other arguments to pass

Details

pairwise.posterior is a fast(er) heuristic alternative to exhaustive search by the function score. For each pair (A,B) of perturbed genes it chooses between four possible models: A..B (unconnected), A->B (superset), A<-B (subset), or A<->B (indistinguishable). The result is the graph built from the maximum aposteriori models for each edge.

print.pairwise gives an overview over the 'pairwise' object.

Value

nem object

Author(s)

Florian Markowetz <URL: http://genomics.princeton.edu/~florian>

See Also

score, nem

Examples

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