essHi-C: Essential component analysis of Hi-C matrices

TL;DR

essHi-C is a novel method that isolates biologically relevant features from Hi-C matrices by separating the essential component from the background noise, improving interpretability and robustness of genome folding analyses.

Contribution

The paper introduces essHi-C, a new approach to extract the essential component of Hi-C matrices, enhancing feature clarity and robustness against sequencing depth.

Findings

Improves clarity of interaction patterns in Hi-C data

Enhances robustness against sequencing depth variations

Enables unsupervised clustering and cell-cycle phase recovery

Abstract

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.