报告题目：Single-cell RNA sequencing data annotation and unseen cell type identification

报告时间：2022年10月21日（周五）下午14:30-16:30

报告链接：#腾讯会议：690-950-4009

报告摘要：Automated cell-type annotation using a well-annotated single-cell RNA-sequencing reference relies on the diversity of cell types in the reference. However, for technical and biological reasons, new query data of interest may contain unseen cell types that are missing from the reference. When annotating new query data, identifying unseen cell types is fundamental not only to improve annotation accuracy but also to new biological discoveries. In this talk, I will introduce a new computational method to automatically annotate query data while accurately identifying unseen cell types with the help of multiple references. Key innovations of this work include the integration of deep learning and ensemble learning to improve prediction accuracy, and the introduction of a new metric to identify unseen cell types. We demonstrate the advantages of mtANN over state-of-the-art methods for cell-type annotation and unseen cell-type identification on two benchmark dataset collections, as well as its predictive power on a collection of COVID-19 datasets.

报告人简介：张晓飞，博士，华中师范大学数学与统计学学院教授，博士生导师。2013年博士毕业于中山大学，同年就职于华中师范大学。目前已主持国家自然科学基金面上项目2项、青年项目1项及湖北省自然科学基金面上项目1项，参与国家重点研发计划“精准医学研究”重点专项1项及国家自然科学基金重点项目1项。已在Bioinformatics（9篇）、Briefings in Bioinformatics、IEEE Transactions等期刊发表学术论文50余篇，论文累计影响因子250左右，被累计引用900余次。开发应用软件20余个。提出的方法和开发的软件被包括欧美院士在内的多位世界知名学者正面评价和使用。担任国家自然科学基金“数学与医疗健康交叉”重点专项、面上项目等项目的通讯评审专家，担任中国自动化学会智能健康与生物信息专委会等委员会委员。