Improving cell-type composition inference in spatial transcriptomics with SpaDAMA.

Accurate determination of cell-type composition in disease-relevant tissues is essential for identifying potential disease targets and understanding tissue heterogeneity. Most current spatial transcriptomics (ST) technologies lack single-cell resolution, which makes precise cell-type composition identification challenging. Several deconvolution methods have been developed to address this limitation by relying on single-cell RNA sequencing (scRNA-seq) data from the same tissue as a reference to estimate the cell type composition in ST data spots. However, these methods often overlook the inherent differences between scRNA-seq and ST data. To overcome this challenge, we introduce a Domain-Adversarial Masked Autoencoder (SpaDAMA) method. SpaDAMA leverages Domain-Adversarial Learning (DAL) to facilitate effective knowledge transfer from the source domain (pseudo-ST data generated from scRNA-seq) to the target domain (real ST data). Through adversarial training, SpaDAMA harmonizes the distributions of both datasets and maps them onto a unified latent representation, thereby reducing discrepancies in data modalities. Furthermore, to strengthen the model's capability in extracting reliable features from real ST data, SpaDAMA employs masking strategies that effectively minimize noise and mitigate spatial artifacts. We validated SpaDAMA on 32 simulated datasets and 4 real-world datasets, demonstrating its superior performance in cell-type deconvolution and providing a promising tool for spatial transcriptomic analyses.