Description Usage Arguments Value References Examples
View source: R/Unweighted_Network.R
This function computes permutation-based FDR of importance scores and returns interactions.
1 | Unweighted_Network(out.iJRF,out.perm,TH)
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out.iJRF |
Output from object of class |
out.perm |
Output from object of class |
TH |
Threshold for FDR. |
List of estimated interactions.
Petralia, F. et al (2017) A new method to study the change of miRNA-mRNA interactions due to environmental exposures, Submitted.
Petralia, F., Wang, P., Yang, J., and Tu Z. (2015) Integrative random forest for gene regulatory network inference. 31(12), i197-i205.
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.
Some of the functions utilized are a modified version of functions contained in R package randomForest: 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 | # --- Generate data sets
nclasses=2 # number of data sets / classes
n1<-n2<-20 # sample size for each data sets
p<-5 # number of response variables
M<-10 # number of predictor variables
W<-abs(matrix(rnorm(M*p),M,p)) # generate sampling scores
Res1<-matrix(rnorm(p*n1),p,n1) # generate response for class 1
Res2<-matrix(rnorm(p*n2),p,n2) # generate response for class 2
Cov1<-matrix(rnorm(M*n1),M,n1) # generate predictors for class 1
Cov2<-matrix(rnorm(M*n2),M,n2) # generate predictors for class 2
# --- Standardize variables to mean 0 and variance 1
Res1 <- t(apply(Res1, 1, function(x) { (x - mean(x)) / sd(x) } ))
Res2 <- t(apply(Res2, 1, function(x) { (x - mean(x)) / sd(x) } ))
# --- Run iJRF and obtain importance score of interactions
out.iJRF<-iJRF(X=list(Cov1,Cov2),Y=list(Res1,Res2),W=W)
# --- Run iJRF for P permutated data sets
out.perm<-iJRF_Perm(X=list(Cov1,Cov2),Y=list(Res1,Res2),W=W,P=2)
# --- Derive final networks
final.net<-Unweighted_Network(out.iJRF,out.perm,0.001)
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