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 |