【工具】COME对比映射学习用于scRNA-seq数据的空间重构
介绍
单细胞RNA测序（scRNA-seq）能够在单细胞分辨率下实现高通量转录组分析。固有的空间位置对于理解单细胞如何协调多细胞功能和驱动疾病至关重要。然而，在组织分离过程中，空间信息常常丢失。空间转录组学（ST）技术可以提供精确的空间基因表达图谱，但其实用性受到其可测定的基因数量或在更大规模上的相关成本以及细粒度细胞类型注释的限制。通过细胞对应学习在scRNA-seq和空间转录组学数据之间传递知识，可以恢复scRNA-seq数据集固有的空间特性。
在本研究中，我们引入了一种名为COME的对比映射学习方法，该方法可以学习ST和scRNA-seq数据之间的映射，从而恢复scRNA-seq数据的空间信息。大量的实验表明，提出的COME方法有效地捕获了精确的细胞-点关系，并且在恢复scRNA-seq数据的空间位置方面优于先前的方法。更重要的是，我们的方法能够精确识别数据中有生物学意义的信息，如缺失基因的空间结构、空间层次模式和每个点的细胞类型组成。这些结果表明，提出的COME方法可以帮助理解组织环境中细胞之间的异质性和活性。
Abstract
Motivation
Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell type annotation. By transferring knowledge between scRNA-seq and spatial transcriptomics data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets.
Results
In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments.
代码
https://github.com/cindyway/COME/blob/main/Tutorial.ipynb
参考
- COME: contrastive mapping learning for spatial reconstruction of scRNA-seq data