北京大学定量生物学中心
学术报告
题 目: Single cell gene fusion detection by scFusion
地 点: 北京大学老化学楼东配楼101报告厅
主持人: 来鲁华 教授
摘 要:
Gene fusions are widespread in tumor cells and can play important roles in tumor initiation and progression. Using full length single cell RNA sequencing (scRNA-seq), gene fusions can now be detected at single cell level by analyzing chimeric reads in scRNA-seq. However, scRNA-seq data has a high noise level and contains various technical artefacts. Direct application of fusion detection tools developed for bulk data can lead to spurious fusion discoveries and leave some true fusions undetected. In this paper, we present a computational tool, scFusion, for gene fusion detection based on scRNA-seq. scFusion is composed of a statistical model and a deep learning model, both of which are designed to control for potential false discoveries. The statistical model models the background noise as zero inflated negative binomial and uses a statistical testing procedure to control for false positives. The deep learning model is trained to recognize technical chimeric artefacts and filter false fusion candidates generated by these artefacts. We compared scFusion with bulk fusion detection methods using simulation data created based on real scRNA-seq data and found that scFusion had superior performance. Applying scFusion to a T cell data, scFusion successfully detected the invariant TCR gene recombinations in Mucosal-associated invariant T cells that many bulk methods failed to detect. In a multiple myeloma data, scFusion detected the known recurrent fusion IgH-WHSC1, which was associated with overexpression of the WHSC1 oncogene.
报告人简介：
席瑞斌，北京大学数学科学学院、统计科学中心研究员。2009年毕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。席瑞斌的主要研究方向是癌基因组及高通量测序数据计算分析、基因组大数据的统计分析、 贝叶斯统计及高维统计。
