differential_subnetwork_analysis_original: Original dGHD Approach for Identifying Differential...

Description Usage Arguments Value Author(s) References See Also

View source: R/generate_mu_std_pval.R

Description

This method identifies the differential sub-network between two graphs using the original dGHD approach of Ruan et al paper.

Usage

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differential_subnetwork_analysis_original(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

Not used in the original approach.

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 <rmall@hbku.edu.qa>

References

Ruan, D., Young, A. and Montana, G., 2015. Differential analysis of biological networks. BMC bioinformatics, 16(1), 327-334.

See Also

differential_subnetwork_analysis_closedform, differential_subnetwork_analysis_fastapprox


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