scTEL: Protein Expression Prediction in Single-cell Analysis Using Transformer

：王超杰（江苏大学）
：2025-05-29 14:30
：海韵园实验楼S208

报告人：王超杰（江苏大学）

时  间：2025年5月29日14:30

地  点：海韵园实验楼S208

内容摘要:

CITE-seq provides a powerful method for simultaneously measuring RNA and protein expression at the single-cell level. The integrated analysis of RNA and protein expression in identical cells is crucial for revealing cellular heterogeneity. However, the high experimental costs associated with CITE-seq limit its widespread application. In this paper, we propose scTEL, a deep learning framework based on Transformer encoder layers, to establish a mapping from sequenced RNA expression to unobserved protein expression in the same cells. This computation-based approach significantly reduces the experimental costs of protein expression sequencing. We are now able to predict protein expression using single-cell RNA sequencing (scRNA-seq) data, which is well-established and available at a lower cost. Moreover, our scTEL model offers a unified framework for integrating multiple CITE-seq datasets, addressing the challenge posed by the partial overlap of protein panels across different datasets. Empirical validation on public CITE-seq datasets demonstrates scTEL significantly outperforms existing methods.

个人简介：

王超杰，江苏大学数学科学学院副教授，硕士生导师，系副主任。2018年博士毕业于香港中文大学统计系，2014年本科毕业于浙江大学数学系。研究方向包括深度学习、统计建模、生物信息学等。以第一或通讯作者在Machine Learning，Expert Systems with Applications，Scandinavian Actuarial Journal等国际期刊发表多篇研究论文。主持国家自然科学基金青年项目1项，江苏省高等学校自然科学研究面上项目1项，入选江苏省“双创”博士、江苏省青年科技人才托举工程等项目。

 

联系人：胡杰