In Cell systems
Single-cell Hi-C (scHi-C) technologies can probe three-dimensional (3D) genome structures in individual cells. However, existing scHi-C analysis methods are hindered by the data quality and complex 3D genome patterns. The lack of computational scalability and interpretability poses further challenges for large-scale analysis. Here, we introduce Fast-Higashi, an ultrafast and interpretable method based on tensor decomposition and partial random walk with restart, enabling joint identification of cell identities and chromatin meta-interactions from sparse scHi-C data. Extensive evaluations demonstrate the advantage of Fast-Higashi over existing methods, leading to improved delineation of rare cell types and continuous developmental trajectories. Fast-Higashi can directly identify 3D genome features that define distinct cell types and help elucidate cell-type-specific connections between genome structure and function. Moreover, Fast-Higashi can generalize to incorporate other single-cell omics data. Fast-Higashi provides a highly efficient and interpretable scHi-C analysis solution that is applicable to a broad range of biological contexts.
Zhang Ruochi, Zhou Tianming, Ma Jian
2022-Oct-19
chromatin meta-interaction, machine learning, single-cell 3D genome, single-cell Hi-C, tensor decomposition
