講座編號：jz-yjsb-2022-y005

講座題目：Single-cell gene fusion detection by scFusion

主 講 人：席瑞斌 北京大學

講座時間：2022年4月8日（星期五）上午10:00

講座地點：騰訊會議，會議ID：791 762 313

參加對象：數學與統計學院全體教師及研究生

主辦單位：數學與統計學院、研究生院

主講人簡介：

席瑞斌，北京大學數學科學學院、統計科學中心研究員，長聘副教授，博士生導師。 2009年畢業于美國圣路易斯華盛頓大學，同年以助理研究員身份加入哈佛大學醫學院從事生物醫學信息學方面的研究。2012年9月加入北京大學。席瑞斌的主要研究方向是生物信息、高維統計、網絡分析、貝葉斯統計、生物醫學大數據、基因組大數據及腫瘤的精準醫學。席瑞斌近年來有40多篇文章發表于PNAS, Science Translational Medicine等高水平的學術期刊。席瑞斌先后主持或參與過科技部973項目、國家重點研發項目、基金委重點項目及基金委面上項目等多個科研基金項目。

主講內容：

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.