文献类型：期刊文献

英文题名：DANST enables cell-type deconvolution in spatial transcriptomics using deep domain adversarial neural networks

作者：Zhang, Xueqin[1];Wu, Zhichao[1];Wang, Tianqi[1];Zhou, Yunlan[2];Ding, Weihong[3];Zhu, Huitong[1];Zhang, Qing[4]

机构：[1]East China Univ Sci & Technol, Sch Informat Sci & Engn, Shanghai, Peoples R China;[2]Shanghai Jiao Tong Univ, Xinhua Hosp, Sch Med, Dept Clin Lab, Shanghai, Peoples R China;[3]Fudan Univ, Huashan Hosp, Shanghai, Peoples R China;[4]Shanghai Inst Technol, Shanghai, Peoples R China

年份：2026

卷号：9

期号：1

外文期刊名：COMMUNICATIONS BIOLOGY

收录：;WOS:【SCI-EXPANDED(收录号:WOS:001717022900004)】；

基金：This work was partially supported by the National Natural Science Foundation of China (NSFC No.51975213) and Clinical Project of Shanghai Municipal Health Commission (No.202340054).

语种：英文

摘要：Spatial transcriptomics is an emerging technology that can analyze gene expression profiles of tissues while preserving spatial location information. To restore cell type proportions from mixed gene expression data, here we present DANST, a deconvolution framework based on deep domain adversarial neural networks. By integrating single-cell RNA sequencing (scRNA-seq) with inferred spatial coordinates, we construct pseudo-spatial data. DANST utilizes a variational autoencoder to learn refined feature representations and introduces a domain adversarial architecture to align feature distributions between pseudo and real data, enabling accurate label transfer. Benchmarking on human and mouse datasets shows that DANST achieves superior deconvolution accuracy compared with existing methods. These findings highlight its effectiveness for tumor microenvironment analysis and potential clinical utility.