讲座编号：jz-yjsb-2022-y005

讲座问题：Single-cell gene fusion detection by scFusion

主 讲 人：席瑞斌 北京大学

讲座时间：2022年4月8日（星期五）上午10:00

讲座所在：腾讯聚会，聚会ID：791 762 313

加入工具：数学与统计学院全体西席及研究生

主理单位：数学与统计学院、研究生院

主讲人简介：

席瑞斌，北京大学数学科学学院、统计科学中心研究员，长聘副教授，博士生导师。 2009年结业于美国圣路易斯华盛顿大学，同年以助理研究员身份加入哈佛大学医学院从事生物医学信息学方面的研究。2012年9月加入北京大学。席瑞斌的主要研究偏向是生物信息、高维统计、网络剖析、贝叶斯统计、生物医学大数据、基因组大数据及肿瘤的精准医学。席瑞斌近年来有40多篇文章揭晓于PNAS, Science Translational Medicine等高水平的学术期刊。席瑞斌先后主持或加入过科技部973项目、国家重点研发项目、基金委重点项目及基金委面上项目等多个科研基金项目。

主讲内容：

Gene fusions can play important roles in tumor initiation and progression. While fusion detection so far has been from bulk samples, full-length single-cell RNA sequencing (scRNA-seq) offers the possibility of detecting gene fusions at the single-cell level. However, scRNA-seq data have a high noise level and contain various technical artifacts that can lead to spurious fusion discoveries. Here, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. We evaluate the performance of scFusion using simulated and five real scRNA-seq datasets and find that scFusion can efficiently and sensitively detect fusions with a low false discovery rate. In a T cell dataset, scFusion detects the invariant TCR gene recombinations in mucosal-associated invariant T cells that many methods developed for bulk data fail to detect; in a multiple myeloma dataset, scFusion detects the known recurrent fusion IgH-WHSC1, which is associated with overexpression of the WHSC1 oncogene. Our results demonstrate that scFusion can be used to investigate cellular heterogeneity of gene fusions and their transcriptional impact at the single-cell level.