Jointly Embedding Multiple Single-Cell Omics Measurements.
Jointly Embedding Multiple Single-Cell Omics Measurements.
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DOI：
10.4230/lipics.wabi.2019.10
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发表时间：
2019-09-03
期刊：
影响因子：
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通讯作者：
Noble, William Stafford
中科院分区：
文献类型：
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作者：
Liu, Jie;Huang, Yuanhao;Noble, William Stafford
现在已经有了许多单细胞测序技术，但将多种测序技术应用于同一单个细胞仍然是困难的。在本文中，我们提出了一种无监督流形比对算法MMD-MA，用于整合在给定细胞群体的不相交等值线上进行的多个测量。有效地，MMD-MA通过将以不同方式测量的细胞嵌入到学习的潜在空间中来执行电子协同分析。在MMD-MA算法中，通过优化具有三个分量的目标函数来对齐来自多个域的单元格数据点：(1)鼓励不同测量点在潜在空间中具有相似分布的最大平均偏差(MMD)项；(2)保持输入空间和潜在空间之间的数据结构的失真项；(3)避免崩溃为平凡解的惩罚项。值得注意的是，MMD-MA不需要跨数据模式的任何对应信息，无论是在小区之间还是在特征之间。此外，MMD-MA对结构域的弱分布要求使该算法能够集成不同类型的单细胞测量，如基因表达、DNA可获得性、染色质组织、甲基化和成像数据。我们在模拟实验和使用涉及单细胞基因表达和甲基化数据的真实数据集上演示了MMD-MA的实用性。
Many single-cell sequencing technologies are now available, but it is still difficult to apply multiple sequencing technologies to the same single cell. In this paper, we propose an unsupervised manifold alignment algorithm, MMD-MA, for integrating multiple measurements carried out on disjoint aliquots of a given population of cells. Effectively, MMD-MA performs an in silico co-assay by embedding cells measured in different ways into a learned latent space. In the MMD-MA algorithm, single-cell data points from multiple domains are aligned by optimizing an objective function with three components: (1) a maximum mean discrepancy (MMD) term to encourage the differently measured points to have similar distributions in the latent space, (2) a distortion term to preserve the structure of the data between the input space and the latent space, and (3) a penalty term to avoid collapse to a trivial solution. Notably, MMD-MA does not require any correspondence information across data modalities, either between the cells or between the features. Furthermore, MMD-MA's weak distributional requirements for the domains to be aligned allow the algorithm to integrate heterogeneous types of single cell measures, such as gene expression, DNA accessibility, chromatin organization, methylation, and imaging data. We demonstrate the utility of MMD-MA in simulation experiments and using a real data set involving single-cell gene expression and methylation data.