COSINE (version 2.1)

GA_search_PPI: Run genetic algorithm to search for the PPI sub-network

Description

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.

Usage

GA_search_PPI(lambda, scaled_node_score, scaled_edge_score, PPI, 
num_iter = 1000, muCh = 0.05, zToR = 10, minsize = 10)

Arguments

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

Value

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

Examples

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)