LogitNet.weight: Derive the weight matrix for fitting the LogitNet model.

In LogitNet: Infer network based on binary arrays using regularized logistic regression

Description

Derive the weight matrix for fitting the LogitNet model.

Usage

LogitNet.weight(X.m, chr)

Arguments

X.m	numeric matrix (n by p). Columns are for genes/loci and rows are for samples. Missing values are not allowed. The genes/loci are ordered according to their positions on the genome.
chr	numeric vector of length p. This vector gives the chromosome information of each gene/locus.

Details

This function returns a weight matrix charactering the spatial correlations along the genome. This matrix provides the value for one input parameter of function LogitNet(). LogitNet is developed for infering interaction network of binary variables. The method is based on penalized logistic regression with an extension to account for spatial correlation in the genomic instability data. (Wang, Chao and Hsu, 2009).

Value

w.s	numeric matrix (p by p), which characterizes the spatial correlation along the genome for each gene/locus.

Author(s)

Pei Wang, Dennis Chao, Li Hsu

References

Pei Wang, Dennis Chao, Li Hsu, "Learning oncogenic pathways from binary genomic instability data", Biometrics, (submitted 2009, July)

Examples

######################## obtain a data example

data(LogitNet.data)
data.m=LogitNet.data$data.m
chromosome=LogitNet.data$chromosome
p=ncol(data.m)

######################## specify the penalty parameter
lambda.n=5
lambda.v=exp(seq(log(13), log(30), length=lambda.n))

######################## calculate the weight matrix
w.m=LogitNet.weight(data.m, chr=chromosome) 

######################## perform cross validation to select lambda
if(0) ### this part will take 10 minutes.
{
try.CV=LogitNet.CV(data.m, w.m, lambda.v, fold=5) 
temp=apply(try.CV[[3]], 2, sum) 
index=which.max(temp) 
}
index=2
######################## estimate the model at selected lambda values

result=LogitNet(data.m, w.m, lambda.v[index]) ###20-30 seconds

######################## illustrate the result similar to Figure 3 of Wang et al. (2009)).

temp=result
diag(temp)=0

par(cex=1.8)
image(1:p, 1:p, temp!=0, col=c("white", "red"), axes=FALSE, xlab="Marker Loci", ylab="Marker Loci")
abline(h=(0:5)*p/6+p/6/2, col=4, lty=3, lwd=0.8)
abline(v=(0:5)*p/6+p/6/2, col=4, lty=3, lwd=0.8)
axis(1, at=c(1,1:6*100), labels=c(1,1:6*100))
axis(2, at=c(1,1:6*100), labels=c(1,1:6*100))
axis(3, at=(0:5)*p/6+p/6/2, labels=c("A", "B", "C", "D", "E", "F"), col.axis=4, tick=FALSE)
axis(4, at=(0:5)*p/6+p/6/2, labels=c("A", "B", "C", "D", "E", "F"), col.axis=4, tick=FALSE)

lab.v=c("A", "B", "C", "D", "E", "F")

cut=30
for(i in 0:4)
{
   cur=i*p/6+p/6/2
   cur2=(i+1)*p/6+p/6/2

   x.cur=c(cur-cut, cur, cur+cut, cur)
   y.cur=c(cur2, cur2-cut, cur2, cur2+cut)
   polygon(x.cur, y.cur, border=grey(0.5))
   polygon(y.cur, x.cur, border=grey(0.5))
}

LogitNet documentation built on May 2, 2019, 1:20 p.m.