Description Usage Arguments Value Author(s) References See Also

View source: R/generate_mu_std_pval.R

This method identifies the differential sub-network between two graphs using the proposed Closed-Form approach of Mall et al paper.

1 2 | ```
differential_subnetwork_analysis_closedform(ghd_val, mu_perm, p,
matrixA, matrixB, threshold)
``` |

`ghd_val` |
Generalized Hamming Distance value calculated using topological graphs of g_A and g_B. |

`mu_perm` |
Asymptotic value of mean permutation for graph g_A. |

`p` |
Represents the number of nodes in graph g_A which is the same as number of nodes in graph g_B. |

`matrixA` |
Topological matrix obtained from graph g_A. |

`matrixB` |
Topological matrix obtained from graph g_B. |

`threshold` |
Threshold after which the "closed-form" technique switches to use a model selection criterion similar to the "original" approach to identify statistically significant changes between two networks. By default its value is 1e-250 and a good range for this value is between 1e-50 to 1e-250. |

A data frame comprising of:

`actual_id` |
Id of a node from the set of nodes in g_A |

`dim_name` |
Name associated with a node from the set of nodes in g_A. |

`p_val` |
P-value associated with that node. |

`ghd_val` |
Generalized Hamming Distance between the topological matrices after removal of that node. |

`mu_perm` |
Asymptotic first order moment: mean value. |

`std_perm` |
Asymptotic second order moment: standard deviation value. |

`V7` |
Adjusted p-value associated with that node. |

Raghvendra Mall <rmall@hbku.edu.qa>

`differential_subnetwork_analysis_original`

, `differential_subnetwork_analysis_fastapprox`

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