  Researchers developed chromosome capture methods such as Hi-C to better
understand DNA's 3D folding in nuclei. The Hi-C method captures contact
frequencies between DNA segment pairs across the genome. When analyzing Hi-C
data sets, it is common to group these pairs using standard bioinformatics
methods (e.g., PCA). Other approaches handle Hi-C data as weighted networks,
where connected node represent DNA segments in 3D proximity. In this
representation, one can leverage community detection techniques developed in
complex network theory to group nodes into mesoscale communities containing
similar connection patterns. While there are several successful attempts to
analyze Hi-C data in this way, it is common to report and study the most
typical community structure. But in reality, there are often several valid
candidates. Therefore, depending on algorithm design, different community
detection methods focusing on slightly different connectivity features may have
differing views on the ideal node groupings. In fact, even the same community
detection method may yield different results if using a stochastic algorithm.
This ambiguity is fundamental to community detection and shared by most complex
networks whenever interactions span all scales in the network. This is known as
community inconsistency. This paper explores this inconsistency of 3D
communities in Hi-C data for all human chromosomes. We base our analysis on two
inconsistency metrics, one local and one global, and quantify the network
scales where the community separation is most variable. For example, we find
that TADs are less reliable than A/B compartments and that nodes with highly
variable node-community memberships are associated with open chromatin.
Overall, our study provides a helpful framework for data-driven researchers and
increases awareness of some inherent challenges when clustering Hi-C data into
3D communities.
