Description Usage Arguments Value References Examples
This function computes importance score in parallel for a subset of target genes.
1 2 |
X |
List object containing expression data for each class, |
W |
|
ntree |
Numeric value: number of trees. If omitted, 1000 trees are considered. |
mtry |
Numeric value: number of predictors to be sampled at each node. If omitted, |
model |
Variable indicating which iJRFNet model needs to be run. Takes values in |
genes.name |
Vector containing genes name. The order needs to match the rows of |
ptm.name |
List of post translational modification variables in protein domain. This list must be ordered as rows of |
parallel |
Vector containing two elements |
List object containing:
num.par |
Integer. Parallel batch implemented. |
model |
Variable indicating which iJRFNet model needs to be run. Takes values in |
importance |
A matrix containing importance score. When function |
Petralia, F., Song, W.M., Tu, Z. and Wang, P. (2016). New method for joint network analysis reveals common and different coexpression patterns among genes and proteins in breast cancer. Journal of proteome research, 15(3), pp.743-754.
A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2, 18–22.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 | # --- Generate data sets
nclasses=2 # number of data sets / classes
n1<-n2<-20 # sample size for each data sets
p<-5 # number of variables (genes)
genes.name<-paste("G",seq(1,p),sep="") # genes name
data1<-matrix(rnorm(p*n1),p,n1) # generate data1
data2<-matrix(rnorm(p*n2),p,n1) # generate data2
# --- Run moultiple jobs and combine them for each function
# -- function iJRF
out.new<-array(0,c(p,p,nclasses))
n.var=0
for (k in 1:3){
out<-iJRFNet_parallel(X=list(data1,data2),genes.name=genes.name,
model="iJRF",parallel=c(k,2))
n.target<-dim(out$importance)[2]
for (c in 1:nclasses) {
out.new[,seq(n.var+1,n.var+n.target),c]<-out$importance[,,c];}
n.var=n.var+n.target
}
# -- function iRafNet
W<-abs(matrix(rnorm(p*p),p,p)) # generate weights for interactions
for (k in 1:3){
out<-iJRFNet_parallel(X=list(data1),W=W,genes.name=genes.name,
model="iRafNet",parallel=c(k,2))
print(dim(out$importance))
if (k==1) out.new<-out$importance
if (k >2) out.new<-cbind(out.new,out$importance)
}
# -- function ptmJRF
genes.name<-paste("G",seq(1,p),sep="") # genes name
ptm.name<-c("G1","G2","G3","G3","G4","G5","G1") # ptm name
p.ptm<-length(ptm.name)
data1<-matrix(rnorm(p.ptm*n2),p.ptm,n1) # generate PTM data
data2<-matrix(rnorm(p*n1),p,n1) # generate global proteomics data
out.new<-array(0,c(p,p,nclasses)) # -- p x p matrix of importance scores
n.var=0
for (k in 1:3){
out<-iJRFNet_parallel(X=list(data1,data2),genes.name=genes.name,
ptm.name=ptm.name,model="ptmJRF",parallel=c(k,2))
n.target<-dim(out$importance)[2]
for (c in 1:nclasses) {
out.new[,seq(n.var+1,n.var+n.target),c]<-out$importance[,,c];}
n.var=n.var+n.target
}
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