differential_subnetwork_analysis_closedform: Closed-Form Approach for Identifying Differential...

In DiffNet: Detection of Statistically Significant Changes in Complex Biological Networks

Description

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

Usage

differential_subnetwork_analysis_closedform(ghd_val, mu_perm, p, 
                                            matrixA, matrixB, threshold)

Arguments

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.

Value

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.

Author(s)

Raghvendra Mall <[e-mail]>

References

https://www.researchgate.net/publication/305355965_Detection_of_statistically_significant_network_changes_in_complex_biological_networks

See Also

differential_subnetwork_analysis_original, differential_subnetwork_analysis_fastapprox

DiffNet documentation built on May 2, 2019, 9:15 a.m.