Unsupervised manifold alignment for single-cell multi-omics data.
Unsupervised manifold alignment for single-cell multi-omics data.
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DOI：
10.1145/3388440.3412410
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发表时间：
2020-09
期刊：
影响因子：
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通讯作者：
Noble WS
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文献类型：
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作者：
Singh R;Demetci P;Bonora G;Ramani V;Lee C;Fang H;Duan Z;Deng X;Shendure J;Disteche C;Noble WS
整合捕捉基因组不同属性的单细胞测量对于扩大我们对基因组生物学的理解至关重要。这项任务具有挑战性，因为从不同类型的单细胞实验获得的数据集缺乏共享的轴。对于大多数这样的数据集，我们缺乏细胞(样本)和测量(特征)之间的对应信息。在这种情况下，能够对单细胞实验进行比对的无监督算法对于学习有助于在细胞之间得出对应关系的计算机协同分析至关重要。基于最大平均差异的流形对齐算法(MMD-MA)就是这样一种无监督算法。在不需要对应信息的情况下，它可以在共同的共享潜在空间中对齐来自不同模式的单细胞数据集，在模拟和61个细胞的小规模单细胞实验中显示出令人振奋的结果。然而，有必要探索这种方法对数千个细胞的更大单细胞实验的适用性，以便它能够引起社区的实际兴趣。在本文中，我们将MMD-MA应用于两个最近的数据集，这两个数据集测量了~2000个单细胞的转录组和染色质的可及性。为了将MMD-MA的运行时间扩展到更多的单元，我们将原始实现扩展到在GPU上运行。我们还介绍了一种自动选择用户定义参数之一的方法，从而减少了超参数搜索空间。我们证明，所提出的扩展允许MMD-MA准确地对齐最先进的单细胞实验。
Integrating single-cell measurements that capture different properties of the genome is vital to extending our understanding of genome biology. This task is challenging due to the lack of a shared axis across datasets obtained from different types of single-cell experiments. For most such datasets, we lack corresponding information among the cells (samples) and the measurements (features). In this scenario, unsupervised algorithms that are capable of aligning single-cell experiments are critical to learning an in silico co-assay that can help draw correspondences among the cells. Maximum mean discrepancy-based manifold alignment (MMD-MA) is such an unsupervised algorithm. Without requiring correspondence information, it can align single-cell datasets from different modalities in a common shared latent space, showing promising results on simulations and a small-scale single-cell experiment with 61 cells. However, it is essential to explore the applicability of this method to larger single-cell experiments with thousands of cells so that it can be of practical interest to the community. In this paper, we apply MMD-MA to two recent datasets that measure transcriptome and chromatin accessibility in ~2000 single cells. To scale the runtime of MMD-MA to a more substantial number of cells, we extend the original implementation to run on GPUs. We also introduce a method to automatically select one of the user-defined parameters, thus reducing the hyperparameter search space. We demonstrate that the proposed extensions allow MMD-MA to accurately align state-of-the-art single-cell experiments.