| COSINE-package | COndition SpecIfic subNEtwork identification |
| choose_lambda | Choose the most appropriate weight parameter lambda |
| cond.fyx | Compute the ECF-statistics measuring the differential correlation of gene pairs |
| COSINE | COndition SpecIfic subNEtwork identification |
| DataSimu | Simulation of the six datasets and the case dataset |
| diff_gen | Calculate the F-statistics and ECF-statistics |
| diff_gen_for3 | Generate the F-statistics and ECF-statistics for the comparison of three datasets |
| diff_gen_PPI | Generate the scaled node score and scaled edge score for nodes and edges in the background network |
| f.test | To get the F-statistics for each gene |
| GA_search | Use genetic algorithm to search for the globally optimal subnetwork |
| GA_search_PPI | Run genetic algorithm to search for the PPI sub-network |
| get_components_PPI | Get all the components (connected clusters) of the sub-network |
| get_quantiles | Get the five quantiles of the weight parameter lambda |
| get_quantiles_PPI | Get the five quantile values of lambda for analysis of gene expression and PPI network data |
| PPI | The protein protein interaction network data |
| random_network_sampling_PPI | To sample random sub-network from the PPI data |
| scaled_edge_score | The scaled ECF statistics of all the edges |
| scaled_node_score | The scaled ECF-statistics of all the edges |
| Score_adjust_PPI | To adjust the score of the selected PPI sub-network using random sampling |
| score_scaling | To get the normalzied F-statistics and ECF-statistics |
| set1_GA | Result of genetic algorithm search for simulated data set1 |
| set1_scaled_diff | The standardized F-statistics and ECF-statistics for the comparison between simulated data1 and the control data |
| set1_unscaled_diff | The unstandardized F-statistics and ECF-statistics of simulated dataset 1 |
| simulated_data | The simulated data sets used in the paper |