Abstract
The 3D organization of chromatin is essential for the functioning of cellular processes, including transcriptional regulation, genome integrity, chromatin accessibility, and higher order nuclear architecture. However, detecting anomalous chromatin interactions in spatiotemporal Hi-C data remains a significant challenge. We present HiC4D-SPOT, an unsupervised deep-learning framework that models chromatin dynamics using a ConvLSTM-based autoencoder to identify structural anomalies. Benchmarking results demonstrate high reconstruction fidelity, with Pearson Correlation Coefficient and Spearman Correlation Coefficient values of 0.9, while accurately detecting deviations linked to temporal inconsistencies, topologically associating domain (TAD) and loop perturbations, and significant chromatin remodeling events. HiC4D-SPOT successfully identifies swapped time points in a time-swap experiment, captures simulated TAD and loop disruptions with high confidence scores and statistical significance of 0.01, and detects HERV-H boundary weakening during cardiomyocyte differentiation, as well as cohesin-mediated loop loss and recovery-aligning with experimentally observed chromatin remodeling events. These findings establish HiC4D-SPOT as an efficient tool for analyzing 3D chromatin dynamics, enabling the detection of biologically significant structural anomalies in spatiotemporal Hi-C data.