Detecting Community Structures in Hi-C Genomic Data
Abstract
Community detection (CD) algorithms are applied to Hi-C data to discover new communities of loci in the 3D conformation of human and mouse DNA. We find that CD has some distinct advantages over pre-existing methods: (1) it is capable of finding a variable number of communities, (2) it can detect communities of DNA loci either adjacent or distant in the 1D sequence, and (3) it allows us to obtain a principled value of k, the number of communities present. Forcing k = 2, our method recovers earlier findings of Lieberman-Aiden, et al. (2009), but letting k be a parameter, our method obtains as optimal value k = 6, discovering new candidate communities. In addition to discovering large communities that partition entire chromosomes, we also show that CD can detect small-scale topologically associating domains (TADs) such as those found in Dixon, et al. (2012). CD thus provides a natural and flexible statistical framework for understanding the folding structure of DNA at multiple scales in Hi-C data.
Cite
@article{arxiv.1509.05121,
title = {Detecting Community Structures in Hi-C Genomic Data},
author = {Irineo Cabreros and Emmanuel Abbe and Aristotelis Tsirigos},
journal= {arXiv preprint arXiv:1509.05121},
year = {2015}
}