  To better understand DNA's 3D folding in cell nuclei, researchers developed
chromosome capture methods such as Hi-C that measure the contact frequencies
between all DNA segment pairs across the genome. As Hi-C data sets often are
massive, it is common to use bioinformatics methods to group DNA segments into
3D regions with correlated contact patterns, such as Topologically Associated
Domains (TADs) and A/B compartments. Recently, another research direction
emerged that treats the Hi-C data as a network of 3D contacts. In this
representation, one can use community detection algorithms from complex network
theory that group nodes into tightly connected mesoscale communities. However,
because Hi-C networks are so densely connected, several node partitions may
represent feasible solutions to the community detection problem but are
indistinguishable unless including other data. Because this limitation is a
fundamental property of the network, this problem persists regardless of the
community-finding or data-clustering method. To help remedy this problem, we
developed a method that charts the solution landscape of network partitions in
Hi-C data from human cells. Our approach allows us to scan seamlessly through
the scales of the network and determine regimes where we can expect reliable
community structures. We find that some scales are more robust than others and
that strong clusters may differ significantly. Our work highlights that finding
a robust community structure hinges on thoughtful algorithm design or method
cross-evaluation.
