LANTSA: Landmark-based transferable subspace analysis for single-cell and spatial transcriptomics

Single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) technologies provide new insights to understand tissue organization and biological function. Accurately capturing the relationships of samples (e.g., sequenced cells, spatial locations) will result in reliable and consistent outcomes in downstream analyses. However, this undertaking remains a challenge for large-volume or cross-platform datasets due to transcriptional heterogeneity and high computational demands. Here, we introduce landmark-based transferable subspace analysis (LANTSA) to solve such challenges for scRNA-seq and ST datasets. Specifically, LANTSA constructs a representation graph of samples for clustering and visualization based on a novel subspace model, which can learn a more accurate representation and is theoretically proven to be linearly proportional to data size in terms of the time consumption. Furthermore, LANTSA uses a dimensionality reduction technique as an integrative method to extract the discriminants underlying the representation structure, which enables label transfer from one (learning) dataset (i.e., scRNA-seq profiles) to the other (prediction) datasets (e.g., scRNA-seq or ST profiles), thus solving the massive-volume or cross-platform problem. We demonstrated the superiority of LANTSA to identify accurate data structures via clustering evaluation on benchmark datasets of various scRNA-seq protocols, 10x Visium, and Slide-seq ST platforms. Moreover, we confirmed the integration capability of LANTSA to transfer cell annotation on large-scale and cross-platform scRNA-seq datasets. Finally, we validated the effectiveness of LANTSA for the identification of multiple mouse brain areas as well as the spatial mapping of cell types within cortical layers by integrating scRNA-seq and ST data.