predictionet.press.statistic: Function computing the press statistic for all target...

Description Usage Arguments Value Author(s) Examples

Description

The function predictionet.press.statistic computes the press statistic for all target variables in the provided topology.

Usage

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predictionet.press.statistic(topo,data,ensemble=FALSE,perturbations=NULL) 

Arguments

topo

adjacency matrix of 0,1 indicating whether two variables are connected

data

matrix of continuous or categorical values (gene expressions for example); observations in rows, features in columns.

perturbations

matrix of 0, 1 specifying whether a gene has been perturbed (e.g., knockdown, overexpression) in some experiments. Dimensions should be the same than data.

ensemble

TRUE if the ensemble approach should be used, FALSE otherwise.

Value

A vector of press statistics, one for every target variable.

Author(s)

Benjamin Haibe-Kains, Catharina Olsen

Examples

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## load gene expression data for colon cancer data, list of genes related to RAS signaling pathway and the corresponding priors
data(expO.colon.ras)
## create matrix of perturbations (no perturbations in this dataset)
pert <- matrix(0, nrow=nrow(data.ras), ncol=ncol(data.ras), dimnames=dimnames(data.ras))

## number of genes to select for the analysis
genen <- 10
## select only the top genes
goi <- dimnames(annot.ras)[[1]][order(abs(log2(annot.ras[ ,"fold.change"])), decreasing=TRUE)[1:genen]]
mydata <- data.ras[ , goi, drop=FALSE]
myannot <- annot.ras[goi, , drop=FALSE]
mypriors <- priors.ras[goi, goi, drop=FALSE]
mydemo <- demo.ras
mypert <- pert[ , goi, drop=FALSE]

########################
## regression-based network inference
########################
## number of fold for cross-validation
res <- netinf.cv(data=mydata, categories=3, perturbations=mypert, priors=mypriors, priors.weight=0.5, method="regrnet", nfold=3, seed=54321)

## MCC for predictions in cross-validation
print(res$prediction.score.cv)

## export network as a 'gml' file that you can import into Cytoscape
## Not run: rr <- netinf2gml(object=res, file="predictionet_regrnet")

########################
## bayesian network inference
########################
## infer a bayesian network network from data and priors
## number of fold for cross-validation
## Not run: res <- netinf.cv(data=mydata, categories=3, perturbations=mypert, priors=mypriors, priors.count=TRUE, priors.weight=0.5, method="bayesnet", nfold=3, seed=54321)

## MCC for predictions in cross-validation
## Not run: print(res$prediction.score.cv)

## export network as a 'gml' file that you can import into Cytoscape
## Not run: rr <- netinf2gml(object=res, file="predictionet_bayesnet")

predictionet documentation built on Nov. 8, 2020, 7:48 p.m.