报告主题：Analyzing population-level single-cell RNA-seq: observations, thoughts, and method development
主讲人简介：
吴浩，国家级海外人才项目获得者，清华大学工学学士学位，约翰霍普金斯大学生物统计学博士学位。归国前为美国埃默里大学生物统计与生物信息学系终身正教授。目前担任中国科学院深圳理工大学计算机科学与控制工程学院杰出教授及中科院深圳先进技术研究院研究员。研究领域是生物统计及生物信息学，主要聚焦于生物医疗大数据（包括高通量基因组学、电子病历、穿戴设备等）的分析处理算法以及临床诊断应用。至2023年6月，吴博士在国际期刊上共发论文100余篇，总引用量16000余次（谷歌学者）。此外，吴博士开发了一系列被广泛应用的开源软件包，包括7个收录于Bioconductor的R语言软件包，每年总下载量超过30000次。
报告摘要：
Single cell genomics technologies have revolutionized the biomedical research, and the development of analytical methods for single cell data has been the most active and cutting-edge area in the computational biology field. A majority of the existing methods were developed based on data with small sample size (one or a few subjects). With the technological advances and cost reduction, people start to perform large-scale, population level single cell experiments.
These data have extra layers of complexities, for example, the demographics and phenotypes of the subjects, experimental design (crossed, nested, paired, longitudinal), etc. Up to now, there is no consensus on the best strategy for analyzing these types of data. In this talk, I will share our recent experiences in analyzing population-level single cell RNA-seq (scRNA-seq) data. I will share our observations, thoughts, and some attempts in method development in several aspects of the data analyses, including cell type identification, differential expression, and rare cell type discovery.
主办单位：人工智能学院
报告时间：2023年6月12日（星期一）9:30
报告地点：正新楼三楼人工智能学院报告厅
