Edges listing and evaluation

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Description

Given a score matrix, this function builds the list of the edges of the associated network. The edges are ordered according to their scores. The score matrix has been computed from a network inference algorithm (e.g. DBNScoreStep1 or DBNScoreStep2, Shrinkage, Lasso, ...). An optional threshold can be specified, as well as a maximal number of edges.

Usage

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BuildEdges(score,threshold=1,nb=NULL,
                  targetNames=NULL,predNames=NULL,prec=3,dec=FALSE)

Arguments

score

matrix with r rows (=target genes) and d columns (=predictor genes) containing the scores resulting from an estimation procedure (e.g. DBNScoreStep1 or DBNScoreStep2, Shrinkage, Lasso, ...).

threshold

An optional real setting the maximal value for edge selection, default=1.

nb

An optional integer setting the maximal number of selected edges, default=NULL.

targetNames

An optional array (r) giving a list of names for the target genes, default=NULL.

predNames

An optional array (d) giving a list of names for the predictor genes, default=NULL.

prec

An optional integer setting the number of decimal places for score display, default=3.

dec

boolean, FALSE if the smallest score points out the most significant edge, default=FALSE.

Value

A matrix containing a list of edges ordered according to the score (First column: predictor, second column: target, third column: corresponding score). Predictors and targets are referred to through the names given by targetNames or predNames when specified.

Author(s)

Lebre Sophie (http://icube-bfo.unistra.fr/en/index.php/Sophie_Lebre),

Chiquet Julien (http://stat.genopole.cnrs.fr/~jchiquet).

See Also

DBNScoreStep1, DBNScoreStep2, BuildNetwork

Examples

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library(G1DBN)
## ======================================
## SIMULATING THE NETWORK

## number of genes
p <- 10
## the network - adjacency Matrix
MyNet <- SimulNetworkAdjMatrix(p,0.05,c(-1.5,-0.5,0.5,1.5))
MyNet

## ======================================
## SIMULATING THE TIME SERIES EXPERIMENTS

## number of time points
n <- 20
## initializing the B vector
B <- runif(p,-1,1)
## initializing the variance of the noise
sigmaEps <- runif(p,0.1,0.5)
## initializing the process Xt
X0 <- B + rnorm(p,0,sigmaEps*10)
## the times series process
Xn <- SimulGeneExpressionAR1(MyNet$A,B,X0,sigmaEps,n)


## ======================================
## NETWORK INFERENCE WITH G1DBN

## STEP 1 - The first step score matrix
S1 <- DBNScoreStep1(Xn, method='ls')

## Building the edges of the network inferred after Step1
alpha1 <- 0.5
G1 <- BuildEdges(S1$S1ls,threshold=alpha1,dec=FALSE)
G1
## STEP 2- The second step score matrix
S2 <- DBNScoreStep2(S1$S1ls, Xn, method='ls', alpha1)

## Building the edges of the network inferred after Step2
alpha2 <- 0.05
G2 <- BuildEdges(S2,threshold=alpha2,dec=FALSE)
G2

## Building the edges of the simulation Graph
Gsimul <- BuildEdges(MyNet$AdjMatrix,threshold=0,dec=TRUE)
Gsimul