SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation
SCOTv2: Single-Cell Multiomic Alignment with Disproportionate Cell-Type Representation
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DOI：
10.1089/cmb.2022.0270
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发表时间：
2022-10
期刊：
影响因子：
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通讯作者：
Pinar Demetci;Rebecca Santorella;Manav Chakravarthy;Bjorn Sandstede;Ritambhara Singh
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作者：
Pinar Demetci;Rebecca Santorella;Manav Chakravarthy;Bjorn Sandstede;Ritambhara Singh
多组单细胞数据使我们能够进行综合分析，以了解生物过程的基因组调控。然而，大多数单细胞测序分析是在单独采样的细胞群体上进行的，因为将它们应用于相同的单细胞是具有挑战性的。现有的无监督单细胞比对算法主要以共分析实验为基准。我们的调查显示，当不同测量领域存在不成比例的细胞类型表示时，这些方法在非协同分析单细胞实验中表现不佳。因此，通过使用不平衡的Gromov-Wasserstein最优传输来处理不成比例的细胞类型表示和单细胞测量中不同的样本大小，我们扩展了我们之前的工作-使用最优传输(SCOT)的单细胞对齐(SCOT)。我们的方法SCOTv2在五个非协同分析数据集(模拟和真实世界)上提供了最先进的比对性能。它还可以集成多个(M≥2)单电池测量，同时保留其原始版本的自我调整能力和计算处理能力。
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 (M≥2) single-cell measurements while preserving the self-tuning capabilities and computational tractability of its original version.