报告题目：Cell-type annotation and missing gene imputation for single-cell spatial transcriptomics using stAI

时间：2024.11.20 14：30-15：30

地点：统计与数据科学学院 106

报告人：张淑芹

报告人简介：张淑芹，复旦大学数学科学学院教授，博士生导师，复旦大学智能复杂体系基础理论与关键技术实验室和应用数学中心双聘教授。主要研究数据驱动的数学、统计模型及算法，尤其是生物医学数据中的相关问题，包括网络数据的结构分析，多种生物医学数据的整合，单细胞数据和空间转录组数据分析方法等。主持和参与国家重点研发、国家自然科学基金、上海市科委“科技创新行动计划”等项目，相关工作发表于Nature communications等期刊。

报告摘要：Spatial transcriptomics technology has revolutionized our understanding of cellular systems by capturing RNA transcript levels in their original spatial context. Single-cell spatial transcriptomics (scST) offers sing-cell resolution expression level and precise spatial information of RNA transcripts, while it has a limited capacity for simultaneously detecting a wide range of RNA transcripts, hindering its broader applications. Characterizing the whole transcriptome level and comprehensively annotating cell types represent two significant challenges in scST applications. Despite several proposed methods for one or both tasks, their performance remains inadequate. In this work, we introduce stAI, a deep learning-based model designed to address both missing gene imputation and cell-type annotation for scST data. stAI leverages a joint embedding for the scST and the reference scRNA-seq data with two separate encoder-decoder modules. Both the imputation and annotation are performed within the latent space in a supervised manner, utilizing scRNA-seq data to guide the processes. Experiments for datasets generated from diverse platforms with varying numbers of measured genes were conducted and compared with the updated methods. The results demonstrate that stAI can predict the unmeasured genes, especially the marker genes, with much higher accuracy and annotate the cell types, including those of small size, with high precision.