GA_search {COSINE} | R Documentation |
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.
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)