JRF_network {JRF} | R Documentation |
This function computes FDR of importance scores and returns class-specific networks.
JRF_network(out.jrf,out.perm,TH)
out.jrf |
output object from function JRF. |
out.perm |
output object from function Run_permutation. |
TH |
Threshold for FDR. |
out
list object containing the estimated gene-gene interactions for each class.
Petralia, F., Song, WM., Tu, Z. and Wang, P., A New Method for Joint Network Analysis Reveals Common and Different Co-Expression Patterns Among Genes and Proteins in Breast Cancer, submitted
A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. R News 2, 18–22.
Xie, Y., Pan, W. and Khodursky, A.B., 2005. A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. Bioinformatics, 21(23), pp.4280-4288.
# --- Derive weighted networks via JRF 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 M=5; # total number of permutations fdr=.001; # fdr threshold # --- Generate data sets data1<-matrix(rnorm(p*n1),p,n1) # generate data1 data2<-matrix(rnorm(p*n2),p,n1) # generate data2 data1[1,]<-2*data1[2,] # variable 1 and 2 interact under class 1 # --- Standardize variables to mean 0 and variance 1 data1 <- t(apply(data1, 1, function(x) { (x - mean(x)) / sd(x) } )) data2 <- t(apply(data2, 1, function(x) { (x - mean(x)) / sd(x) } )) # --- Run JRF and obtain importance score of interactions for each class out<-JRF(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name) out.perm<-Run_permutation(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name,M) final.net<-JRF_network(out,out.perm,fdr)