hic2community: Create module objects from the Hi-C data

In HiCociety: Inferring Chromatin Interaction Modules from 3C-Based Data

Create module objects from the Hi-C data

Description

It generates a list of graph of significant interactions, module table and module elements.

Usage

hic2community(
  fname,
  chr,
  resol,
  nbprob,
  farthest,
  par.noise = 1,
  network.cluster.method = "louvain",
  n_cores = NULL
)

Arguments

fname	Path to .hic file
chr	chromosome numbers to run.
resol	Resolution of Hi-C data
nbprob	Negative binomial probability. Higher value gives smaller number of stronger interaction.
farthest	The maximum searching distance between two nodes
par.noise	Parameter for noise removal. Default is 1, higher value gives more filtered interactions.
network.cluster.method	Can select between 'louvain' as default and 'label_prop' which means the label propagation method.
n_cores	The number of cores used for parallel computing. If set as NULL, n_cores is automatically set to the number of cores in the computer if it is not exceed 30. If it is more than 30, it is set as 30. Default = NULL

Details

It generates a list of graph of significant interactions, module table and module elements.

Value

A list containing three elements: Graphs (an igraph object representing significant chromatin interactions for each chromosome), ModuleSummary (a data.frame containing information about chromatin interaction modules), and ModuleElements (a list of nodes forming significant chromatin interactions within each module).

Author(s)

Sora Yoon, PhD

Examples

# This example might take a long time to run, so we wrap it in donttest{}

myhic=system.file('extdata', 'example.hic', package ='HiCocietyExample')
mycom = hic2community(myhic, "19", 5000, 0.975, 2000000,
par.noise=1, 'louvain', n_cores=2)

HiCociety documentation built on Feb. 16, 2026, 5:10 p.m.