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

TL;DR

SCUDDO is an unsupervised clustering algorithm that effectively groups single-cell Hi-C maps, outperforming existing methods, especially in challenging datasets with high sparsity, without requiring prior cell type labels.

Contribution

We developed SCUDDO, a novel unsupervised algorithm that improves clustering accuracy of single-cell Hi-C maps by capturing latent features without supervision.

Findings

SCUDDO outperforms existing algorithms in ARI by over 0.2 on difficult datasets.

SCUDDO maintains high accuracy even with limited contact data.

SCUDDO effectively identifies cell types without prior labels.

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.