Run_permutation {JRF} | R Documentation |
This function computes importance score for M
permuted data sets. Sample labels of target genes are randomly permuted and JRF is implemented. Resulting importance scores can be used to derive an estimate of FDR.
Run_permutation(X, ntree, mtry,genes.name,M)
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 |
M |
integer: total number of permutations. |
A three dimensional matrix (I
x M
x C
) with I
being the number of total interactions, M
the number of permutations and C
the number of classes. Element (i,j,k)
corresponds to the importance score for interaction i
, permuted data j
and 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 M=5; # --- 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<-Run_permutation(list(data1,data2),mtry=round(sqrt(p-1)),ntree=1000,genes.name,M)