COndition SpecIfic subNEtwork identification
The scaled ECF statistics of all the edges
The protein protein interaction network data
Get the five quantiles of the weight parameter lambda
Calculate the F-statistics and ECF-statistics
random_network_sampling_PPI
To sample random sub-network from the PPI data
Generate the scaled node score and scaled
edge score for nodes and edges in the background network
Choose the most appropriate weight parameter lambda
Generate the F-statistics and ECF-statistics for the
comparison of three datasets
Get the five quantile values of lambda for analysis
of gene expression and PPI network data
The scaled ECF-statistics of all the edges
Result of genetic algorithm search for simulated data set1
To get the normalzied F-statistics and ECF-statistics
Get all the components (connected clusters) of the sub-network
The simulated data sets used in the paper
Run genetic algorithm to search for the PPI sub-network
Simulation of the six datasets and the case dataset
The unstandardized F-statistics and ECF-statistics of simulated dataset 1
Compute the ECF-statistics measuring the differential
correlation of gene pairs
Use genetic algorithm to search for the globally optimal subnetwork
To get the F-statistics for each gene
To adjust the score of the selected PPI
sub-network using random sampling
The standardized F-statistics and ECF-statistics for the comparison
between simulated data1 and the control data