GA_search_PPI {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 PPI subnetwork across several datasets.
GA_search_PPI(lambda, scaled_node_score, scaled_edge_score, PPI, num_iter = 1000, muCh = 0.05, zToR = 10, minsize = 10)
lambda |
One of the five quantiles of the weight parameter lambda |
scaled_node_score |
A vector storing the F-statistics measuring the differential expression of each gene, which length equals the number of genes N |
scaled_edge_score |
A vector storing the ECF-statistics measuring the differential correlation of each gene pair |
PPI |
A two-column matrix containing the protein interaction pairs |
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 |
minsize |
The minimal size of selected sub-network |
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
data(scaled_node_score) data(scaled_edge_score) data(PPI) GA_result<-GA_search_PPI(lambda=0.5,scaled_node_score,scaled_edge_score,PPI, num_iter=1, muCh=0.05, zToR=10, minsize=50)