Description Usage Arguments Value References Examples
This function computes importance score for M
permuted data sets. Sample labels of target genes are randomly permuted and iRafNet is implemented. Resulting importance scores can be used to derive an estimate of FDR.
1  Run_permutation(X, W, ntree, mtry,genes.name,M)

X 

W 

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 matrix with I
rows and M
columns with I
being the total number of regulations and M
the number of permutations. Element (i,j)
corresponds to the importance score of interaction i
for permuted data j
.
Petralia, F., Wang, P., Yang, J., Tu, Z. (2015) Integrative random forest for gene regulatory network inference, Bioinformatics, 31, i197i205.
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.743754.
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  #  Generate data sets
n<20 # sample size
p<5 # number of genes
genes.name<paste("G",seq(1,p),sep="") # genes name
M=5; # number of permutations
data<matrix(rnorm(p*n),n,p) # generate expression matrix
W<abs(matrix(rnorm(p*p),p,p)) # generate score for regulatory relationships
#  Standardize variables to mean 0 and variance 1
data < (apply(data, 2, function(x) { (x  mean(x)) / sd(x) } ))
#  Run iRafNet and obtain importance score of regulatory relationships
out.iRafNet<iRafNet(data,W,mtry=round(sqrt(p1)),ntree=1000,genes.name)
#  Run iRafNet for M permuted data sets
out.perm<Run_permutation(data,W,mtry=round(sqrt(p1)),ntree=1000,genes.name,M)

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