SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport
SCOT: Single-Cell Multi-Omics Alignment with Optimal Transport
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DOI：
10.1089/cmb.2021.0446
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发表时间：
2022-01-01
影响因子：
1.7
通讯作者：
Singh, Ritambhara
中科院分区：
文献类型：
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作者：
Demetci, Pinar;Santorella, Rebecca;Singh, Ritambhara
测序技术的最新进展使我们能够以单细胞分辨率捕获基因组的各个方面。然而，除了少数共测定技术之外，不可能在相同的单细胞上同时应用不同的测序测定。在这种情况下，多组学测量的计算集成对于实现联合分析至关重要。由于缺乏样本或特征对应关系，该集成任务尤其具有挑战性。我们提出了单细胞对齐与最佳运输（SCOT），一种无监督的算法，使用Gromov-Wasserstein最佳运输来对齐单细胞多组学数据集。SCOT的性能与当前最先进的无监督对齐方法相当，速度更快，并且需要调整更少的超参数。更重要的是，SCOT使用自调整启发式来指导基于Gromov-Wasserstein距离的超参数选择。因此，在完全无监督的环境中，SCOT比现有方法更好地对齐单细胞数据集，而不需要任何正交对应信息。
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.