报告人：孙端辰教授，山东大学数学学院

报告题目：基于多组学数据的表型特异细胞亚群识别算法研究

报告时间：2024年11月20日 9:30-10:30

报告地点：腾讯会议 368 817 476

报告摘要：

Single-cell RNA sequencing (scRNA-seq) distinguishes cell types, states and lineages within the context of heterogeneous tissues. However, current single-cell data cannot directly link cell clusters with specific phenotypes. Here we present Scissor, a method that identifies cell subpopulations from single-cell data that are associated with a given phenotype. Scissor integrates phenotype-associated bulk expression data and single-cell data by first quantifying the similarity between each single cell and each bulk sample. It then optimizes a regression model on the correlation matrix with the sample phenotype to identify relevant subpopulations. Applied to a lung cancer scRNA-seq dataset, Scissor identified subsets of cells

associated with worse survival and with TP53 mutations. In melanoma, Scissor discerned a T cell subpopulation with low PDCD1/CTLA4 and high TCF7 expression associated with an immunotherapy response. Beyond cancer, Scissor was effective in interpreting facioscapulohumeral muscular dystrophy and Alzheimer's disease datasets. Scissor identifies biologically and clinically relevant cell subpopulations from single-cell assays by leveraging phenotype and bulk-omics datasets.

报告人简介：

孙端辰，山东大学数学学院教授、博士生导师。2017年毕业于中国科学院数学与系统科学研究院运筹学与控制论专业。主要从事数学、计算机与前沿生物医学问题的交叉研究。以第一或共同第一作者在 Nature Biotechnology、Molecular Cancer、Nature Communications、Briefings in Bioinformatics、Clinical Cancer Research等期刊上发表文章。目前主持国家自然科学基金项目1项，参与国家重点研发计划课题1项、国自然重点项目1项。担任中国运筹学会计算系统生物学分会理事，中国人工智能学会生物信息学与人工生命专业委员会委员，中国计算机学会生物信息学专委会通讯委员等学术职务。