Simultaneous alignment of cells and features of unpaired single-cell multi-omics datasets with co-optimal transport

Pinar Demetci, Quang Huy TRAN, Ievgen Redko, Ritambhara Singh

Keywords: single-cell multi-omics, data integration, regulatory genomics, optimal transport, manifold alignment

TL;DR: Optimal-transport-based method to align both cells and features of unpaired single-cell multi-omic measurements

Abstract: Availability of different single-cell multi-omic datasets provide an opportunity to study various aspects of the genome at the single-cell resolution. Jointly studying multiple genomic features can help us understand gene regulatory mechanisms. Although there are experimental challenges to jointly profile multiple genomic features on the same single-cell, computational methods have been develop to align unpaired single-cell multi-omic datasets. Despite the success of these alingment methods, studying how genomic features interact in gene regulation requires the alignment of features, too. However, most single-cell multi-omic alignment tools can only align cells across different measurements. Here, we introduce \textsc{SCOOTR}, which aligns both cells and features of the single-cell multi-omic datasets. Our preliminary results show that \textsc{SCOOTR} provides quality alignments for datasets with sparse correspondences, and for datasets with more complex relationships, supervision on one level (e.g. cells) improves alignment performance on the other level (e.g. features).