STRIDE: A Sequencing Depth Robust Metric for Sparse Chromosome Conformation Capture Data Comparison

High-throughput chromatin conformation capture technologies, such as Hi-C and its derivatives, have revolutionized the study of 3D genome organization. However, the robustness of contact-frequency-based analyses to sequencing depth remains a major challenge, particularly for sparse datasets such as single-cell Hi-C. To overcome this challenge, we present a novel computational framework that employs the mean first passage time (MFPT) from Markov chain theory to convert chromatin contact matrices into a graph-based effective distance representation. We demonstrate that the MFPT representation is empirically more robust to sequencing-depth reduction and supports reliable downstream 3D genomic analyses, including TAD and chromatin loop detection, differential chromatin contact analysis, as well as the imputation and representation of sparse single-cell Hi-C data. Building on this representation, we developed STRIDE (spatial topological representation of interaction distance evaluation), a library similarity metric that does not require dataset-specific parameter optimization. STRIDE is robust to sparse and noisy data and shows improved resilience to technical variation, thereby facilitating accurate identification of cellular states and developmental trajectories in the datasets analyzed. Together, the MFPT representation and STRIDE provide a robust computational framework for sequencing-depth-resilient analysis of 3D genome organization.