SpaJoint: a transfer learning method for spatial transcriptomics deconvolution

主讲人：许欣怡

许欣怡，中央财经大学统计与数学学院副教授，中国人民大学统计学博士。主要研究方向为单细胞多组学数据分析、深度学习在生物信息中的应用、高维复杂数据分析等。在Nature Communications、Briefings in Bioinformatics等国际学术期刊发表多篇论文。主持国家自然科学基金青年基金项目、国家统计局统计科学研究项目等多项课题。

1  报告信息

时间 

2026年4月14日(周二)

14:00

地点 

中国人民大学通州校区经济学部楼215会议室

2  报告摘要

Currently, many widely used spatial transcriptomics (ST) technologies do not achieve single-cell resolution, with each spot capturing signals from multiple, potentially heterogeneous cells. As a result, a key challenge is to resolve the spatial distribution of distinct cell types within tissues, which is fundamental for understanding tissue architecture and biological function. Here we present a deconvolution method based on transfer learning, SpaJoint. This method integrates gene expression derived from single-cell RNA sequencing (scRNA-seq) and ST, taking into account the spatial correlation across locations of different spots. Comprehensive experiments demonstrate that SpaJoint achieves excellent performance in predicting the cell-type composition of spatial spots and identifying the spatial regions of cell types, thus highly effective and broadly applicable among various scRNA-seq and ST datasets. Additionally, it exhibits remarkable robustness to hyperparameters and provides significant advantage in computational efficiency.