Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
Department of Computer Science, Brown University, Providence, Rhode Island, USA.
J Comput Biol. 2022 Jan;29(1):19-22. doi: 10.1089/cmb.2021.0477. Epub 2022 Jan 5.
Although the availability of various sequencing technologies allows us to capture different genome properties at single-cell resolution, with the exception of a few co-assaying technologies, applying different sequencing assays on the same single cell is impossible. Single-cell alignment using optimal transport (SCOT) is an unsupervised algorithm that addresses this limitation by using optimal transport to align single-cell multiomics data. First, it preserves the local geometry by constructing a -nearest neighbor (-NN) graph for each data set (or domain) to capture the intra-domain distances. SCOT then finds a probabilistic coupling matrix that minimizes the discrepancy between the intra-domain distance matrices. Finally, it uses the coupling matrix to project one single-cell data set onto another through barycentric projection, thus aligning them. SCOT requires tuning only two hyperparameters and is robust to the choice of one. Furthermore, the Gromov-Wasserstein distance in the algorithm can guide SCOT's hyperparameter tuning in a fully unsupervised setting when no orthogonal alignment information is available. Thus, SCOT is a fast and accurate alignment method that provides a heuristic for hyperparameter selection in a real-world unsupervised single-cell data alignment scenario. We provide a tutorial for SCOT and make its source code publicly available on GitHub.
尽管各种测序技术的出现使得我们能够以单细胞分辨率捕获不同的基因组特性，但除了少数联合检测技术外，在同一个单细胞上应用不同的测序检测是不可能的。单细胞最优传输对齐（SCOT）是一种无监督算法，它通过使用最优传输来对齐单细胞多组学数据来解决这一限制。首先，它通过为每个数据集（或域）构建一个近邻（-NN）图来保留局部几何结构，以捕获域内距离。然后，SCOT 找到一个概率耦合矩阵，该矩阵最小化了域内距离矩阵之间的差异。最后，它使用耦合矩阵通过重心投影将一个单细胞数据集投影到另一个数据集上，从而对它们进行对齐。SCOT 只需要调整两个超参数，并且对其中一个的选择具有鲁棒性。此外，算法中的 Gromov-Wasserstein 距离可以在没有正交对齐信息的情况下，在完全无监督的设置中指导 SCOT 的超参数调整。因此，SCOT 是一种快速准确的对齐方法，为实际无监督单细胞数据对齐场景中的超参数选择提供了一种启发式方法。我们提供了一个关于 SCOT 的教程，并在 GitHub 上公开了它的源代码。
