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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
Event URL: https://openreview.net/forum?id=iXIyOS2dJ3 »

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).

Author Information

Pinar Demetci (Brown University)
Quang Huy TRAN (Université Bretagne Sud)
Ievgen Redko (Aalto University)
Ritambhara Singh (Brown University)

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