predictionet (version 1.18.0)

predictionet.press.statistic: Function computing the press statistic for all target variables in topology

Description

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

Usage

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.

Examples

## 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")