GA_search_PPI: Run genetic algorithm to search for the PPI sub-network
In COSINE: COndition SpecIfic sub-NEtwork

Description Usage Arguments Value Author(s) 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 PPI subnetwork across several datasets.

1 2	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)