separation_Significance: Separation Significance

In NetSci: Calculates Basic Network Measures Commonly Used in Network Medicine

Separation Significance

Description

Calculates the separation of two set of targets on a network and assigns a p-value to it. Often used to measure separation of disease modules in a interactome. Separation is calculated as in Menche, J. et al (2015) <doi:10.1126/science.1257601>. p-values are calculates based on the permutation of nodes, you can set the full network to be in the set for permutation or can select the ones you include as input.

Usage

separation_Significance(G, ST, Threads = 2, N = 1000, correct_by_target = TRUE)

Arguments

G	The original graph (often an interactome / PPI).
ST	Set-Target data. It is a data.frame with two columns. ID and Target.
Threads	How many threads you'd like to use (for parallel computation).
N	default to 1000. The number of permutations
correct_by_target	TRUE by default. If you want to use the set of targets for the permutation or the full network.

Examples

set.seed(12)
require(magrittr)
x = data.frame(n1 = sample(LETTERS[1:5]),
               n2 =  sample(LETTERS[1:20]))

D1 = data.frame(gene = c("H", "I", "S", "N", "A"), disease = "D1")
D2 = data.frame(gene = c("E", "C",  "R" , "J", "Q", "O"), disease = "D2")
D3 = data.frame(gene = c("E", "G", "T", "P"), disease = "D3")
D4 = data.frame(gene = c("A", "B", "E"), disease = "D4")
D5 = data.frame(gene = c("D", "F", "L"), disease = "D5")
D6 = data.frame(gene = c("D", "F", "K"), disease = "D6")
D7 = data.frame(gene = c("A", "B" ,"F", "K"), disease = "D7")

Diseases = rbind(D1, D2, D3, D4, D5, D6, D7)
Diseases %<>% dplyr::select(disease, gene)
g = igraph::graph_from_data_frame(x, directed = FALSE)
g = igraph::simplify(g)

separation_Significance(G = g,
ST = Diseases,
correct_by_target = FALSE,
Threads = 2)

NetSci documentation built on July 4, 2022, 1:05 a.m.