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

ABSTRACT: 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

SUBMITTER: Huang L 

PROVIDER: S-EPMC12393736 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature