nem (version 2.46.0)

nem.consensus: 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,control=set.default.parameters(unique(colnames(D))), verbose=TRUE)
"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, triples for triple-based inference, pairwise for the pairwise heuristic, ModuleNetwork for the module based inference, nem.greedy for greedy hillclimbing, nem.greedyMAP for alternating MAP optimization using log odds or log p-value densities

models

a list of adjacency matrices for model search. If NULL, an exhaustive enumeration of all possible models is performed.

control

list of parameters: see set.default.parameters

verbose

do you want to see progression statements? Default: TRUE

x

nem object

...

other arguments to pass

Value

consensus network (nem object)

Details

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

See Also

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

Examples

## Not run: 
#    data("BoutrosRNAi2002")
#    D <- BoutrosRNAiDiscrete[,9:16]   
#    nem.consensus(D, control=set.default.parameters(unique(colnames(D)), para=c(0.13,0.05)))            
# ## End(Not run)