Quick run of Nested Effects Models inference

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Description

Interface to learn NEM models from data

Usage

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quicknem(D,type="CONTmLLDens",inference="nem.greedy",controls.name=NULL,contrasts=NULL,normalize=FALSE,cutoff=0.05,DIR="bum",plot=TRUE,bootstrap=0,...) 

Arguments

D

ExpressionSet object or data matrix with raw or normalized expression data.

type

Parameter estimation, either mLL, FULLmLL, CONTmLL, CONTmLLBayes, CONTmLLMAP, depn.

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

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

P-value 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 or FULLmLL or CONTmLL or CONTmLLBayes or CONTmLLMAP or depn. CONTmLLDens and CONTmLLRatio are identical to CONTmLLBayes and CONTmLLMAP and are still supported for compatibility reasons. mLL and FULLmLL are used for binary data (see BoutrosRNAiDiscrete) and CONTmLL for a matrix of effect probabilities. CONTmLLBayes and CONTmLLMAP are used, if log-odds ratios, p-value 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, and CONTmLLMAP 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 to triples, pairwise, ModuleNetwork, nem.greedy or nem.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 using type="depn", where neither controls.name nor contrasts 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 using type="depn", where neither controls.name nor contrasts needs to be defined.

DIR

In case of type="CONTmLLDens" or type="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 E-gene subset

LLperGene

likelihood per selected E-gene

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, nem.bootstrap will be called internally.

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

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## 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("rel-control","rel-LPS","key-control","key-LPS","tak-control","tak-LPS","mkk4hep-control","mkk4hep-LPS")
res <- quicknem(BoutrosRNAiExpression,contrasts=contrasts)

data(SahinRNAi2008)
dat <- dat.unnormalized #[,sample(1:17,5)]
res <- quicknem(dat,type="depn")

## End(Not run)

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