nem.consensus {nem}R Documentation

Statistically stabile nested effects models

Description

Performs bootstrapping (resampling with replacement) on E-genes and jackknife on S-genes to assess the statistical stability of networks. Only edges appearing with a higher frequency than a predescribed threshold in both procedures are regarded as statistical stable and appear in the so-called consensus network.

Usage

nem.consensus(D,thresh=0.5,nboot=1000,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,delta=1,selEGenes=FALSE,verbose=TRUE)

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

Arguments

D data matrix with experiments in the columns (binary or continous)
thresh only edges appearing with a higher frequency than "thresh" in both, bootstrap and jackknife procedure, are regarded as statistically stable and trust worthy
nboot number of bootstrap samples desired
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; or nem.greedy for greedy hillclimbing
models a list of adjacency matrices for model search
type mLL or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP, seenem
para vector of length two: false positive rate and false negative rate for 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 include prior assumptions
delta regularization parameter for automated E-gene subset selection (CONTmLLMAP only)
selEGenes automated E-gene subset selection (includes tuning of delta for CONTmLLMAP)
verbose do you want to see progression statements? Default: TRUE
x nem object
... other arguments to pass

Details

Calls nem or nemModelSelection internally, depending on whether or not lambda is a vector and Pm != NULL.

Value

consensus network (nem object)

Author(s)

Holger Froehlich

See Also

nem.bootstrap, nem.jackknife, nem.calcSignificance, nem

Examples

## Not run: 
   data("BoutrosRNAi2002")
   D <- BoutrosRNAiDiscrete[,9:16]
   p <- c(.13,.05)
   nem.consensus(D, para=p)            
## End(Not run)

[Package nem version 2.6.0 Index]