Description Usage Arguments Value References Examples

This function computes permutation-based FDR of importance scores and returns gene-gene regulations.

1 | ```
iRafNet_network(out.iRafNet,out.perm,TH)
``` |

`out.iRafNet` |
Output object from function |

`out.perm` |
Output object from function |

`TH` |
Threshold for FDR. |

List of estimated regulations.

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.

A. Liaw and M. Wiener (2002). Classification and Regression by randomForest. *R News* **2**, 18–22.

Xie, Y., Pan, W. and Khodursky, A.B., 2005. A note on using permutation-based false discovery rate estimates to compare different analysis methods for microarray data. *Bioinformatics*, **21**(23), pp.4280-4288.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# --- 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 gene expression matrix
data[,1]<-data[,2] # var 1 and var 2 interact
W<-abs(matrix(rnorm(p*p),p,p)) # generate weights 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(p-1)),ntree=1000,genes.name)
# --- Run iRafNet for M permuted data sets
out.perm<-Run_permutation(data,W,mtry=round(sqrt(p-1)),ntree=1000,genes.name,M)
# --- Derive final networks
final.net<-iRafNet_network(out.iRafNet,out.perm,0.001)
``` |

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