nem {nem}R Documentation

Nested Effects Models - main function

Description

The main function to infer a phenotypic hierarchy from data

Usage

nem(D,inference="nem.greedy",models=NULL,type="mLL",para=NULL,hyperpara=NULL,Pe=NULL,Pm=NULL,Pmlocal=NULL,local.prior.size=length(unique(colnames(D))),local.prior.bias=1,triples.thrsh=0.5,lambda=0,selEGenes=FALSE,verbose=TRUE)

Arguments

D data matrix with experiments in the columns (binary or continious)
inference search to use exhaustive enumeration; or triples for triple-based inference; or pairwise for the pairwise heuristic; or ModuleNetwork for the module based inference
models a list of adjacency matrices for model search. If NULL, enumerate.models is used for exhaustive enumeration of all possible models.
type mLL or FULLmLL or CONTmLL or CONTmLLDens or CONTmLLRatio
para vector of length two: false positive rate and false negative rate for non-binary data. Used by mLL()
hyperpara vector of length four: used by FULLmLL() for binary data
Pe prior of effect reporter positions in the phenotypic hierarchy (same dimension as D)
Pm prior over models (n x n matrix)
Pmlocal local model prior for pairwise and triple learning. For pairwise learning generated by local.model.prior() according to arguments local.prior.size and local.prior.bias
local.prior.size prior expected number of edges in the graph (for pairwise learning)
local.prior.bias bias towards double-headed edges. Default: 1 (no bias; for pairwise learning)
triples.thrsh threshold for model averaging to combine triple models for each edge
lambda regularization parameter to incorporate prior assumptions. Default: 0 (no regularization)
selEGenes optimize selection of E-genes for each model
verbose do you want to see progression statements" Default: TRUE

Details

nem is an interface to the functions score(), pairwise.posterior(), triple.posterior, moduleNetwork, nem.greedy.

plot.nem plots the inferred phenotypic hierarchy as a directed graph, the likelihood distribution of the models (only for exhaustive search) or the posterior position of the effected genes.

Value

An object of class 'score' or 'pairwise' or 'triples' or 'ModuleNetwork' containing slots

graph the inferred phenotypic hierarchy
pos posterior distribution of positions of effect reporters
mappos estimated position of effects in the phenotypic hierarchy
type see above
para see above
hyperpara see above
lambda see above

and additional ones according to the function used for inference.

Author(s)

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

See Also

score, moduleNetwork, nem.greedy, triples.posterior, pairwise.posterior, local.model.prior, enumerate.models, plot.nem

Examples

   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]
   p <- c(.13,.05)
   res1 <- nem(D,para=p,inference="search")
   res2 <- nem(D,para=p,inference="pairwise")
   res3 <- nem(D,para=p,inference="triples")
   res4 <- nem(D,para=p,inference="ModuleNetwork")
   res5 <- nem(D,para=p,inference="nem.greedy")
   

   par(mfrow=c(1,4))
   plot(res1,main="exhaustive search")
   plot(res2,main="pairs")
   plot(res3,main="triples")
   plot(res4,main="module network")
   plot(res5,main="greedy hillclimber")
   

[Package nem version 2.2.1 Index]