Community detection (CD) algorithms are applied to Hi-C data to discover newcommunities of loci in the 3D conformation of human and mouse DNA. We find thatCD has some distinct advantages over pre-existing methods: (1) it is capable offinding a variable number of communities, (2) it can detect communities of DNAloci either adjacent or distant in the 1D sequence, and (3) it allows us toobtain a principled value of k, the number of communities present. Forcing k =2, our method recovers earlier findings of Lieberman-Aiden, et al. (2009), butletting k be a parameter, our method obtains as optimal value k = 6,discovering new candidate communities. In addition to discovering largecommunities that partition entire chromosomes, we also show that CD can detectsmall-scale topologically associating domains (TADs) such as those found inDixon, et al. (2012). CD thus provides a natural and flexible statisticalframework for understanding the folding structure of DNA at multiple scales inHi-C data.
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