JRF_permutation {JRF} | R Documentation |
This function computes importance score for one permuted data set. Sample labels of target genes are randomly permuted and JRF is implemented. Resulting importance scores can be used to derive an estimate of FDR.
JRF_permutation(X, ntree, mtry,genes.name,perm)
X |
List object containing expression data for each class, |
ntree |
numeric value: number of trees. |
mtry |
numeric value: number of predictors to be sampled at each node. |
genes.name |
vector containing genes name. The order needs to match the rows of |
perm |
integer: seed for permutation. |
A matrix with I
rows and C
columns with I
being the number of total interactions and C
the number of classes. Element (i,k)
corresponds to the importance score for interaction i
under class k
.
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.
# --- 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 perm=1; # set permutation seed # --- Generate data sets data1<-matrix(rnorm(p*n1),p,n1) # generate data1 data2<-matrix(rnorm(p*n2),p,n1) # generate data2 # --- 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_permutation(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name,perm)