SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation

Journal of computational biology: A journal of computational molecular cell biology

article

Pinar Demetci¹  Rebecca Santorella³  Manav Chakravarthy²  Bjorn Sandstede³  Ritambhara Singh¹ 

[1] Center for Computational Molecular Biology, Brown University;Department of Computer Science, Brown University;Division of Applied Mathematics, Brown University

关键词: data integration;    manifold alignment;    multiomics;    single-cell sequencing;    unbalanced optimal transport;   

DOI  :  10.1089/cmb.2022.0270

学科分类：生物科学（综合）

来源: Mary Ann Liebert, Inc. Publishers

【 摘 要 】

Multiomic single-cell data allow us to perform integrated analysis to understand genomic regulation of biological processes. However, most single-cell sequencing assays are performed on separately sampled cell populations, as applying them to the same single-cell is challenging. Existing unsupervised single-cell alignment algorithms have been primarily benchmarked on coassay experiments. Our investigation revealed that these methods do not perform well for noncoassay single-cell experiments when there is disproportionate cell-type representation across measurement domains. Therefore, we extend our previous work—Single Cell alignment using Optimal Transport (SCOT)—by using unbalanced Gromov-Wasserstein optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. Our method, SCOTv2, gives state-of-the-art alignment performance across five non-coassay data sets (simulated and real world). It can also integrate multiple () single-cell measurements while preserving the self-tuning capabilities and computational tractability of its original version.

【 授权许可】

Unknown