SCUDDO: An unsupervised clustering algorithm for single-cell Hi-C maps using diagonal diffusion operators

Abstract

Motivation: Advances in high-throughput chromatin conformation capture have provided insight into the three-dimensional structure and organization of chromatin. While bulk Hi-C experiments capture spatio-temporally averaged chromatin interactions across millions of cells, single-cell Hi-C experiments report on the chromatin interactions of individual cells. Supervised and unsupervised algorithms have been developed to embed single-cell Hi-C maps and identify different cell types. However, single-cell Hi-C maps are often difficult to cluster due to their high sparsity, with state-of-the-art algorithms achieving a maximum Adjusted Rand Index (ARI) of only < 0.4 on several datasets while requiring labels for training. Results: We introduce a novel unsupervised algorithm, Single-cell Clustering Using Diagonal Diffusion Operators (SCUDDO), to embed and cluster single-cell Hi-C maps. We evaluate SCUDDO on three previously difficult-to-cluster single-cell Hi-C datasets, and show that it can outperform other current algorithms in ARI by > 0.2. Further, SCUDDO outperforms all other tested algorithms even when we restrict the number of intrachromosomal maps for each cell type and when we use only a small fraction of contacts in each Hi-C map. Thus, SCUDDO can capture the underlying latent features of single-cell Hi-C maps and provide accurate labeling of cell types even when cell types are not known a priori. Availability: SCUDDO is freely available at www.github.com/lmaisuradze/scuddo. The tested datasets are publicly available and can be downloaded from the Gene Expression Omnibus.