  Motivation: Hi-C matrices are cornerstones for qualitative and quantitative
studies of genome folding, from its territorial organization to compartments
and topological domains. The high dynamic range of genomic distances probed in
Hi-C assays reflects in an inherent stochastic background of the interactions
matrices, which inevitably convolve the features of interest with largely
aspecific ones. Results: Here we introduce a discuss essHi-C, a method to
isolate the specific, or essential component of Hi-C matrices from the
aspecific portion of the spectrum that is compatible with random matrices.
Systematic comparisons show that essHi-C improves the clarity of the
interaction patterns, enhances the robustness against sequencing depth, allows
the unsupervised clustering of experiments in different cell lines and recovers
the cell-cycle phasing of single-cells based on Hi-C data. Thus, essHi-C
provides means for isolating significant biological and physical features from
Hi-C matrices.
