G1DBN (version 3.1.1)

ROCcurve: ROC curves computation

Description

Given a score matrix and a validation matrix, this function allows to compute the corresponding ROC curve by returning a list with the respective $x$ and $y$ coordinates of the ROC curve. The score matrix has been computed from a network inference algorithm (e.g. DBNScoreStep1 or DBNScoreStep2, Shrinkage, Lasso, ...).

Usage

ROCcurve(score,validMat,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, ...).

validMat

An optional matrix specifying the validated edges (1 if an edge is validated, 0 otherwise).

dec

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

Value

$x$ and the $y$ coordinates of the ROC curve.

See Also

DBNScoreStep1, DBNScoreStep2, PRcurve.

Examples

## generate the validation matrix
## number of genes
p <- 20
## the network - adjacency Matrix
MyNet <- SimulNetworkAdjMatrix(p,0.05,c(-1,0.5,0.5,1))

## generate the time series 
## initializing the B vector
B <- runif(p,-1,1)
## initializing the variance of the noise
sigmaEps <- runif(p,0.05,0.5)
## initializing the process Xt
X0 <- B + rnorm(p,0,sigmaEps*10)
## number of time points
n <- 20

## the AR(1) times series process
Xn <- SimulGeneExpressionAR1(MyNet$A,B,X0,sigmaEps,n)


## compute score S1 
out<-DBNScoreStep1(Xn)
roc1<-ROCcurve(score=out$S1ls,validMat=abs(MyNet$AdjMatrix)>0,dec=FALSE)

## compute score S2 from S1 
## depending on the generated data, the threshold alpha1 has to be chosen differently.
alpha1=0.8
S2<-DBNScoreStep2(S1=out$S1ls,data=Xn,alpha1=alpha1)
roc2_0.8<-ROCcurve(score=S2,validMat=abs(MyNet$AdjMatrix)>0,dec=FALSE)

alpha1=0.6
S2<-DBNScoreStep2(S1=out$S1ls,data=Xn,alpha1=alpha1)
roc2_0.6<-ROCcurve(score=S2,validMat=abs(MyNet$AdjMatrix)>0,dec=FALSE)

alpha1=0.4
S2<-DBNScoreStep2(S1=out$S1ls,data=Xn,alpha1=alpha1)
roc2_0.4<-ROCcurve(score=S2,validMat=abs(MyNet$AdjMatrix)>0,dec=FALSE)

alpha1=0.2
S2<-DBNScoreStep2(S1=out$S1ls,data=Xn,alpha1=alpha1)
roc2_0.2<-ROCcurve(score=S2,validMat=abs(MyNet$AdjMatrix)>0,dec=FALSE)


TP=sum(abs(MyNet$AdjMatrix)>0)
FN=p^2-TP

plot(roc1$x/FN,roc1$y/TP,type="l", main="ROC curve after both Step1 and Step2",
     ylab="True Positive Rate", xlab="False Negative Rate",lwd=2,lty=2)
lines(roc2_0.8$x/FN, roc2_0.8$y/TP, col=3,lwd=2)
lines(roc2_0.6$x/FN, roc2_0.6$y/TP, col=4,lwd=2)
lines(roc2_0.4$x/FN, roc2_0.4$y/TP, col=5,lwd=2)
lines(roc2_0.2$x/FN, roc2_0.2$y/TP, col=6,lwd=2)
lines(0:1,c(0,0),lty=3)
lines(0:1,c(1,1),lty=3)
lines(c(0,0),0:1,lty=3)
lines(c(1,1),0:1,lty=3)
leg=c("Step 1", "Step 2 (alpha1=0.8)", "Step 2 (alpha1=0.6)",
      "Step 2 (alpha1=0.4)", "Step 2 (alpha1=0.2)")
legend(0.568,0.265, leg, lty=c(2,1,1,1,1), col=c(1,3,4,5,6),lwd=array(2,5))