MINTsC learns multi-way chromatin interactions from single cell high throughput chromatin conformation data

A number of foundational analysis methods have emerged for single cell high throughput chromatin conformation (scHi-C) datasets capturing 3D organizations of genomes with pairwise measurements at the single cell or nuclei resolution; however, these datasets are currently under-utilized. The canonical analyses of scHi-C data encompass, beyond standard cell type identification, inference of chromosomal structures and pairwise interactions. However, multi-way chromatin interactions among genomic elements are entirely overlooked. We introduce MINTsC to learn Multi-way INTeractions from single cell Hi-C. MINTsC builds on a dirichlet-multinomial spline model and yields multi-way interaction scores by aggregating pairwise interactions across cells of a context and summarizing them using order statistics of pairwise test statistics. MINTsC yields well-calibrated p-values for controlling the false discovery rate. Evaluation of MINTsC with scHi-C datasets from cell lines and complex tissues using multiple external genomic and epigenomic datasets support multi-way interactions inferred by MINTsC. Application of MINTsC to scHi-C data from human prefrontal cortex revealed multi-way chromatin interactions with biological implications of gene regulation by multiple enhancers. Most notably, MINTsC-inferred multi-way interactions demonstrate its potential for probing molecular QTL and association studies for epistatic SNP effects by significantly reducing the multiple-testing burden. MINTsC is publicly available at https://github.com/keleslab/mintsc.