Description Usage Arguments Value References Examples
iJRF infers interactions across two different sets of genomic variables for different class of data. iJRF borrows information across multiple class of data while taking into account prior information from existing databases. As an example, iJRF can be used to infer microRNA-mRNA interactions for different data sets corresponding to different treatment conditions while taking into account information from existing microRNA-mRNA databases.
1 |
X |
List object containing predictors for each class, |
Y |
List object containing response variables for each class, |
W |
|
ntree |
Numeric value: number of trees. If omitted, |
mtry |
Numeric value: number of predictors to be sampled at each node. If omitted, |
res.name |
p-dimensional vector containing names of response variable. |
cov.name |
M-dimensional vector containing names of predictors. |
A matrix with I
rows and C + 2
columns where I=M x p
is the total number of interactions and C
is the number of classes. The first two columns contain variables name for each interaction while the remaining columns contain importance scores for different classes.
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 | # --- 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(X=list(Cov1,Cov2),Y=list(Res1,Res2),W=W)
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