COME: contrastive mapping learning for spatial reconstruction of scRNA-seq data.

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.

Availability and implementation: The COME is freely available in GitHub (https://github.com/cindyway/COME).

Supplementary information: Supplementary data are available at Bioinformatics online.