一个由scRNA-seq和空间转录组学的未配对数据组成的联合模型，用于填补缺失的基因表达测量值
A joint model of unpaired data from scRNA-seq and spatial transcriptomics for imputing missing gene expression measurements
摘要：
转录组的空间研究为生物学家提供了异质性和复杂组织的基因表达图谱。然而，空间转录组学的大多数实验方案都需要预先选择一小部分基因在整个转录组中进行定量。标准的单细胞RNA测序（scRNA-seq）更普遍，更容易实施，原则上可以捕获任何基因，但不能恢复细胞的空间位置。在这篇手稿中，我们专注于基于来自相同生物组织的（未配对的）标准scRNA-seq数据的空间转录组数据中缺失基因的填补问题。在领域自适应工作的基础上，我们提出了gimVI，这是一种用于整合空间转录组数据和scRNA-seq数据的深度生成模型，可用于估算缺失基因。在描述了我们的生成模型和推理过程之后，我们将gimVI与计算生物学或真实数据集上的领域自适应的替代方法进行了比较，并优于Seurat Adherors，Liger和CORAL来估算保留的基因。
英文摘要：
Spatial studies of transcriptome provide biologists with gene expression maps of heterogeneous and complex tissues. However, most experimental protocols for spatial transcriptomics suffer from the need to select beforehand a small fraction of genes to be quantified over the entire transcriptome. Standard single-cell RNA sequencing (scRNA-seq) is more prevalent, easier to implement and can in principle capture any gene but cannot recover the spatial location of the cells. In this manuscript, we focus on the problem of imputation of missing genes in spatial transcriptomic data based on (unpaired) standard scRNA-seq data from the same biological tissue. Building upon domain adaptation work, we propose gimVI, a deep generative model for the integration of spatial transcriptomic data and scRNA-seq data that can be used to impute missing genes. After describing our generative model and an inference procedure for it, we compare gimVI to alternative methods from computational biology or domain adaptation on real datasets and outperform Seurat Anchors, Liger and CORAL to impute held-out genes.
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