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SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation
[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.
[发布日期]  [发布机构] 
[效力级别]  [学科分类] 生物科学(综合)
[关键词] data integration;manifold alignment;multiomics;single-cell sequencing;unbalanced optimal transport [时效性] 
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