Unsupervised Integration of Single-Cell Multi-omics Datasets with Disproportionate Cell-Type Representation
Unsupervised Integration of Single-Cell Multi-omics Datasets with Disproportionate Cell-Type Representation
复制标题
具有不成比例的细胞类型表示的单细胞多组学数据集的无监督整合
DOI：
10.1007/978-3-031-04749-7_1
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Singh, Ritambhara
中科院分区：
文献类型：
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作者：
Demetçi, Pinar;Santorella, Rebecca;Sandstede, Bjorn;Singh, Ritambhara
多组学数据的综合分析允许研究基因组中的不同分子视图如何相互作用以调节细胞过程;然而，除了少数例外，不可能在同一个单细胞上应用多个测序测定。虽然最近的无监督算法对齐单细胞多组学数据集，但这些方法主要是基于共测定实验，而不是从单独采样的细胞群中进行的更常见的单细胞实验。因此，大多数现有的方法在这样的数据集上执行低于标准杆的对齐。在这里，我们通过使用不平衡的最佳传输来处理不成比例的细胞类型表示和跨单细胞测量的不同样本大小，从而改进了我们以前的使用最佳传输（SCOT）的单细胞对齐工作。我们表明，我们提出的方法，SCOTv 2，始终产生质量的比对五个真实世界的单细胞数据集与不同的细胞类型的比例，是计算上容易处理。此外，我们扩展了SCOTv 2以整合多个（）单细胞测量，并提出了一个自调整启发式过程，以在没有任何正交对应信息的情况下选择超参数。http://rsinghlab.github.io/SCOT
Integrated analysis of multi-omics data allows the study of how different molecular views in the genome interact to regulate cellular processes; however, with a few exceptions, applying multiple sequencing assays on the same single cell is not possible. While recent unsupervised algorithms align single-cell multi-omic datasets, these methods have been primarily benchmarked on co-assay experiments rather than the more common single-cell experiments taken from separately sampled cell populations. Therefore, most existing methods perform subpar alignments on such datasets. Here, we improve our previous work Single Cell alignment using Optimal Transport (SCOT) by using unbalanced optimal transport to handle disproportionate cell-type representation and differing sample sizes across single-cell measurements. We show that our proposed method, SCOTv2, consistently yields quality alignments on five real-world single-cell datasets with varying cell-type proportions and is computationally tractable. Additionally, we extend SCOTv2 to integrate multiple () single-cell measurements and present a self-tuning heuristic process to select hyperparameters in the absence of any orthogonal correspondence information.Available at:http://rsinghlab.github.io/SCOT.