SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport

• Published in: Journal of computational biology Vol. 29; no. 1; p. 3
• Main Authors: Demetci, Pinar, Santorella, Rebecca, Sandstede, Björn, Noble, William Stafford, Singh, Ritambhara
• Format: Journal Article
• Language: English
• Published: United States 01.01.2022
• Subjects: Algorithms; Computational Biology; Computer Simulation; Databases, Genetic - statistics & numerical data; Genomics - statistics & numerical data; Humans; Models, Statistical; Sequence Alignment - statistics & numerical data; Single-Cell Analysis - statistics & numerical data; Unsupervised Machine Learning; multi-omics; optimal transport; single-cell genomics; manifold alignment; data integration
• ISSN: 1557-8666
• DOI: 10.1089/cmb.2021.0446

Recent advances in sequencing technologies have allowed us to capture various aspects of the genome at single-cell resolution. However, with the exception of a few of co-assaying technologies, it is not possible to simultaneously apply different sequencing assays on the same single cell. In this scenario, computational integration of multi-omic measurements is crucial to enable joint analyses. This integration task is particularly challenging due to the lack of sample-wise or feature-wise correspondences. We present single-cell alignment with optimal transport (SCOT), an unsupervised algorithm that uses the Gromov-Wasserstein optimal transport to align single-cell multi-omics data sets. SCOT performs on par with the current state-of-the-art unsupervised alignment methods, is faster, and requires tuning of fewer hyperparameters. More importantly, SCOT uses a self-tuning heuristic to guide hyperparameter selection based on the Gromov-Wasserstein distance. Thus, in the fully unsupervised setting, SCOT aligns single-cell data sets better than the existing methods without requiring any orthogonal correspondence information.