Multisource Single-Cell Data Integration by MAW Barycenter for Gaussian Mixture Models
Multisource Single-Cell Data Integration by MAW Barycenter for Gaussian Mixture Models
复制标题
MAW Barycenter 用于高斯混合模型的多源单细胞数据集成
作者：
Lin, Lin;Shi, Wei;Ye, Jianbo;Li, Jia
单细胞数据聚类中遇到的一个关键挑战是将从多个源获取的数据集的聚类结果联合收割机组合。我们建议代表每个数据集的聚类结果的高斯混合模型（GMM），并产生一个综合的结果的基础上的概念Wasserstein重心。然而，精确的重心的高斯分布，在相同的样本空间，是计算上不可行的解决。重要的是，GMM的重心可能不是包含合理数量的组件的GMM。因此，我们建议使用最小化的聚合Wasserstein（MAW）的距离来近似Wasserstein度量和开发一个新的算法计算的重心下MAW的Gynecology。最近的理论进展进一步证明了使用MAW距离作为Gynecology之间的Wasserstein度量的近似。我们还证明了Gynesian的MAW重心与Wasserstein重心具有相同的期望。我们提出的算法聚类集成规模以及与数据维度和混合成分的数量，与数据大小无关的复杂性。我们证明了新方法在几个单细胞RNA-seq数据集上取得了比其他一些流行方法更好的聚类结果。
One key challenge encountered in single‐cell data clustering is to combine clustering results of data sets acquired from multiple sources. We propose to represent the clustering result of each data set by a Gaussian mixture model (GMM) and produce an integrated result based on the notion of Wasserstein barycenter. However, the precise barycenter of GMMs, a distribution on the same sample space, is computationally infeasible to solve. Importantly, the barycenter of GMMs may not be a GMM containing a reasonable number of components. We thus propose to use the minimized aggregated Wasserstein (MAW) distance to approximate the Wasserstein metric and develop a new algorithm for computing the barycenter of GMMs under MAW. Recent theoretical advances further justify using the MAW distance as an approximation for the Wasserstein metric between GMMs. We also prove that the MAW barycenter of GMMs has the same expectation as the Wasserstein barycenter. Our proposed algorithm for clustering integration scales well with the data dimension and the number of mixture components, with complexity independent of data size. We demonstrate that the new method achieves better clustering results on several single‐cell RNA‐seq data sets than some other popular methods.