Unsupervised topological alignment for single-cell multi-omics integration
Unsupervised topological alignment for single-cell multi-omics integration
复制标题
用于单细胞多组学集成的无监督拓扑比对
DOI：
10.1093/bioinformatics/btaa443
复制
发表时间：
2020-07-01
期刊：
影响因子：
5.8
通讯作者：
Wan, Lin
中科院分区：
文献类型：
--
作者：
Cao, Kai;Bai, Xiangqi;Wan, Lin
单细胞多组学数据提供了细胞的全面分子视图。然而，单细胞多组学数据集由未配对的细胞组成，这些细胞在不同的模态中具有不同的不匹配特征，这使得数据集成具有挑战性。在这项研究中，我们提出了一种新的算法，称为UnionCom，用于单细胞多组学集成的无监督拓扑对齐。UnionCom不需要任何对应信息，无论是单元之间还是功能之间。它首先将每个单细胞数据集的内在低维结构嵌入到同一数据集内的细胞距离矩阵中，然后通过矩阵优化方法匹配距离矩阵来对齐单细胞多组学数据集上的细胞。最后，它将单个细胞数据集上不同的不匹配特征投影到一个公共的嵌入空间中，以实现对齐细胞的特征可比性。为了在数据集之间匹配复杂的非线性几何扭曲的低维结构，UnionCom提出并调整了距离矩阵的全局缩放参数，用于对齐相似的拓扑结构。它不需要跨数据集的细胞之间的一一对应，并且它可以容纳具有特定于细胞类型的样本。UnionCom在模拟和真实的单细胞多组学数据集上的表现优于最先进的方法。UnionCom对参数选择以及特征的二次采样具有鲁棒性。UnionCom软件可在https://github.com/caokai1073/UnionCom上获得。
Single-cell multi-omics data provide a comprehensive molecular view of cells. However, single-cell multi-omics datasets consist of unpaired cells measured with distinct unmatched features across modalities, making data integration challenging. In this study, we present a novel algorithm, termed UnionCom, for the unsupervised topological alignment of single-cell multi-omics integration. UnionCom does not require any correspondence information, either among cells or among features. It first embeds the intrinsic low-dimensional structure of each single-cell dataset into a distance matrix of cells within the same dataset and then aligns the cells across single-cell multi-omics datasets by matching the distance matrices via a matrix optimization method. Finally, it projects the distinct unmatched features across single-cell datasets into a common embedding space for feature comparability of the aligned cells. To match the complex nonlinear geometrical distorted low-dimensional structures across datasets, UnionCom proposes and adjusts a global scaling parameter on distance matrices for aligning similar topological structures. It does not require one-to-one correspondence among cells across datasets, and it can accommodate samples with dataset-specific cell types. UnionCom outperforms state-of-the-art methods on both simulated and real single-cell multi-omics datasets. UnionCom is robust to parameter choices, as well as subsampling of features. UnionCom software is available at https://github.com/caokai1073/UnionCom.