triples.posterior {nem}R Documentation

Infers a phenotypic hierarchy from triples

Description

Function triples.posterior estimates the hierarchy triple-wise. In each step only a triple of nodes is involved and no exhaustive enumeration of model space is needed as in function score.

Usage

triples.posterior(D, type="mLL",para=NULL, hyperpara=NULL,Pe=NULL,Pmlocal=NULL,Pm=NULL,lambda=0,delta=1, triples.thrsh=.5,verbose=TRUE)

## S3 method for class 'triples':
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
triples.thrsh threshold used when combining tripel models for each edge. Default: only edges appearing in more than half of triples are included in the final graph.
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

triples.posterior is an alternative to exhaustive search by the function score and more accurate than pairwise.posterior. For each triple of perturbed genes it chooses between the 29 possible models. It then uses model averaging to combine the triple-models into a final graph.

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

Value

nem object

Author(s)

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

References

Markowetz F, Kostka D, Troyanskaya OG, Spang R: Nested effects models for high-dimensional phenotyping screens. Bioinformatics. 2007; 23(13):i305-12.

See Also

score, nem

Examples

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