GA_search: Use genetic algorithm to search for the globally optimal...
In COSINE: COndition SpecIfic sub-NEtwork

Description Usage Arguments Value Author(s) References Examples

This function performs the stochastic search using genetic algorithm to find the globally optimal subnetwork which gives rise to the highest score defined by a scoring function, which measures the extent of the differential expression of the subnetwork across several datasets.

1 2	GA_search(lambda, diff_expr, diff_coex, num_iter = 1000, muCh = 0.05, zToR = 10)

`lambda`	A vector containing the five quantiles of the weight parameter lambda
`diff_expr`	A vector storing the F-statistics measuring the differential expression of each gene, which length equals the number of genes N
`diff_coex`	An N by N matrix with entry (i,j) corresponding to the ECF-statistics of gene pair (i,j), which measures the differential correlation between genes i and j
`num_iter`	The number of iterations to be performed by the genetic algorithm
`muCh`	the mutation chance used by genetic algorithm
`zToR`	zero to one ratio

A list containing the following components:

`Subnet_size`	A vector containing the size of the subnetwork identified using each lambda
`Best_Scores`	A vector containing the best scores of the subnetworks
`Subnet`	A list containing the extracted subnetworks (a list of genes) for each of the five lambda values
`GA_obj`	A list of the returned objects of the genetic algorithm function

Haisu Ma

http://cran.r-project.org/web/packages/genalg/index.html

# Load the scaled F-statistics and ECF-statistics 
# for the simulated datasets

data(set1_scaled_diff)

# Get the quantiles of lambda

klist<-c(25,30)
set1_quantile<-get_quantiles(diff_expr=set1_scaled_diff[[1]], 
diff_coex=set1_scaled_diff[[2]],klist,pop_size=10)
lambda<-set1_quantile[[2]]

#Perform genetic algorithm to search-just show the first iteration here

set1_GA<-GA_search(lambda[1:2],diff_expr=set1_scaled_diff[[1]],
diff_coex=set1_scaled_diff[[2]], num_iter=1, muCh=0.05, zToR=50)