Quick run of Nested Effects Models inference
Description
Interface to learn NEM models from data
Usage
1 
Arguments
D 
ExpressionSet object or data matrix with raw or normalized expression data. 
type 
Parameter estimation, either 
inference 

controls.name 
Pattern to search for in the columnnames of D. Defines which columns in D should be regarded as controls. 
contrasts 
String defining the contrasts to estimate via limma 
normalize 
boolean value, should quantile normalization be performed 
cutoff 
Pvalue cutoff for differential expression using adjusted p.values from limma. 
DIR 
Directory name, where additional informative plots should be stored. Created if not present. 
plot 
Should the inferred network be plotted? 
bootstrap 
Integer defining the number of bootstrapping samples to be performed. Defaults to 0. 
... 
other arguments to pass 
Details
Wrapper function for call of nem inference. Extracts differential genes for given contrasts and infers a NEM  graph for the given inference type.
 D
Is either an ExpressionSet Object or a matrix/data.frame containing the expression values from the siRNA knockdown experiments. If an ExpressionSet, the data is extracted via exprs(ExpressionSet). The knockdowns must be in the columns, the measured effect genes in the rows of the expression matrix.
 type
mLL
orFULLmLL
orCONTmLL
orCONTmLLBayes
orCONTmLLMAP
ordepn
.CONTmLLDens
andCONTmLLRatio
are identical toCONTmLLBayes
andCONTmLLMAP
and are still supported for compatibility reasons.mLL
andFULLmLL
are used for binary data (seeBoutrosRNAiDiscrete
) andCONTmLL
for a matrix of effect probabilities.CONTmLLBayes
andCONTmLLMAP
are used, if logodds ratios, pvalue densities or any other model specifies effect likelihoods.CONTmLLBayes
refers to an inference scheme, were the linking positions of effect reporters to network nodes are integrated out, andCONTmLLMAP
to an inference scheme, were a MAP estimate for the linking positions is calculated.depn
indicates Deterministic Effects Propagation Networks (DEPNs). inference
Type of network reconstruction.
search
enumerates all possible networks. Set totriples
,pairwise
,ModuleNetwork
,nem.greedy
ornem.greedyMAP
for heuristic search of the network. controls.name
Defines a pattern to search for in the column names of D, which describes the control experiments. Each remaining experiment is then compared via limma to these controls by defining the appropriate contrasts. If NULL, then
controls.name
must be given, except for usingtype="depn"
, where neithercontrols.name
norcontrasts
needs to be defined. contrasts
Defines the contrasts of interest that should be used for the limma analysis. If NULL, then
controls.name
must be given, except for usingtype="depn"
, where neithercontrols.name
norcontrasts
needs to be defined. DIR
In case of
type="CONTmLLDens"
ortype="CONTmLLBayes"
some additional plots for the BUM model fits are created and stored here.
Value
graph 
the inferred directed graph (graphNEL object) 
mLL 
log posterior marginal likelihood of final model 
pos 
posterior over effect positions 
mappos 
MAP estimate of effect positions 
selected 
selected Egene subset 
LLperGene 
likelihood per selected Egene 
control 
hyperparameter as in function call 
bootstrap 
Integer number defining how many bootstrap samples should be drawn. If 0, no bootstrapping will be performed. Else, 
Author(s)
Christian Bender, Holger Froehlich, Florian Markowetz
See Also
nem
, set.default.parameters
, nemModelSelection
, nem.jackknife
, nem.bootstrap
, nem.consensus
, local.model.prior
, plot.nem
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14  ## Not run:
data(BoutrosRNAi2002)
exps < colnames(BoutrosRNAiExpression)
res < quicknem(BoutrosRNAiExpression,controls="control")
res < quicknem(BoutrosRNAiExpression,controls="control",type="CONTmLLRatio")
res < quicknem(BoutrosRNAiExpression,controls="control",type="CONTmLLRatio",inference="ModuleNetwork")
contrasts < c("relcontrol","relLPS","keycontrol","keyLPS","takcontrol","takLPS","mkk4hepcontrol","mkk4hepLPS")
res < quicknem(BoutrosRNAiExpression,contrasts=contrasts)
data(SahinRNAi2008)
dat < dat.unnormalized #[,sample(1:17,5)]
res < quicknem(dat,type="depn")
## End(Not run)
