Reconstruction of the cell pseudo-space from single-cell RNA sequencing data with scSpace
Jingyang Qian, Jie Liao, Ziqi Liu, Ying Chi, Fang Yin, Yanrong Zheng, Xin Shao, B. Liu, Yongjin Cui, Wenbo Guo, Yining Hu, Hudong Bao, Penghui Yang, Qian Chen, Mingxiao Li, Bing Zhang, Xiaohui Fan
Abstract
Tissues are highly complicated with spatial heterogeneity in gene expression. However, the cutting-edge single-cell RNA-seq technology eliminates the spatial information of individual cells, which contributes to the characterization of cell identities. Herein, we propose single-cell spatial position associated co-embeddings (scSpace), an integrative method to identify spatially variable cell subpopulations by reconstructing cells onto a pseudo-space with spatial transcriptome references (Visium, STARmap, Slide-seq, etc.). We benchmark scSpace with both simulated and biological datasets, and demonstrate that scSpace can accurately and robustly identify spatially variated cell subpopulations. When employed to reconstruct the spatial architectures of complex tissue such as the brain cortex, the small intestinal villus, the liver lobule, the kidney, the embryonic heart, and others, scSpace shows promising performance on revealing the pairwise cellular spatial association within single-cell data. The application of scSpace in melanoma and COVID-19 exhibits a broad prospect in the discovery of spatial therapeutic markers.