SA2E: spatial-aware auto-encoder for cell type deconvolution of spatial transcriptomics data

Spatial transcriptomics (ST) technologies measure gene expression together with spatial locations, but each spot typically contains a mixture of cell types, posing a challenge for downstream analysis. Cell-type deconvolution aims to infer spot-wise cell-type proportions by integrating single-cell RNA-seq (scRNA-seq) and ST data. Many existing methods construct cell-type signatures from predefined marker genes, which can limit performance when marker information is incomplete or unavailable.

To address this limitation, we propose a spatial-aware auto-encoder framework (SA2E) for cell-type deconvolution without requiring predefined cell-type biomarkers. SA2E learns latent spot representations using a spatially regularized auto-encoder that preserves the local topology of the spot spatial graph. Based on these representations, SA2E learns cell-type signatures by enforcing them to reconstruct ST expression. In our framework, simulated ST data with known proportions are used for supervised pretraining, while real ST data are optimized using the reconstruction objective. Extensive experiments on simulated and real ST datasets demonstrate that SA2E outperforms state-of-the-art deconvolution baselines.

The code of SA2E is available at Github (https://github.com/xkmaxidian/SA2E) and Zenodo (DOI: 10.5281/zenodo.18765467).