Abstract
Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers single-cell resolution expression level and precise spatial information of RNA transcripts, while it has a limited capacity for simultaneously detecting a wide range of RNA transcripts, hindering its broader applications. Characterizing the whole transcriptome level and comprehensively annotating cell types represent two significant challenges in scST applications. Despite several proposed methods for one or both tasks, their performance remains inadequate. In this work, we introduce stAI, a deep learning-based model designed to address both missing gene imputation and cell-type annotation for scST data. stAI leverages a joint embedding for the scST and the reference scRNA-seq data with two separate encoder-decoder modules. Both the imputation and annotation are performed within the latent space in a supervised manner, utilizing scRNA-seq data to guide the processes. Experiments for datasets generated from diverse platforms with varying numbers of measured genes were conducted and compared with the updated methods. The results demonstrate that stAI can predict the unmeasured genes, especially the marker genes, with much higher accuracy, and annotate the cell types, including those of small size, with high precision.