演讲摘要：Efficient single cell assignment without prior marker gene annotations is essential for single cell sequencing data analysis. Current methods, however, have limited effectiveness for distinct single cell assignment. They failed to achieve a well-generalized performance in different tasks due to the inherent heterogeneity of different single cell sequencing datasets and different single cell types. Furthermore, current methods are inefficient to identify novel cell types that are absent in the reference datasets. To this end, we present scLearn, a metric learning-based framework that automatically infers quantitative measurement/similarity and threshold that can be used for different single cell assignment tasks, achieving a well-generalized assignment performance on different single cell types. We evaluated scLearn on a comprehensive set of publicly available benchmark datasets. We proved that scLearn outperformed the comparable existing methods for single cell assignment from various aspects, demonstrating state-of-the-art effectiveness with a reliable and generalized single cell type identification and categorizing ability.
讲者简介：同济大学生物信息系教授，博士生导师。致力于基于人工智能和高通量组学数据分析，进行肿瘤精准用药、药物发现、肿瘤免疫治疗及基因编辑领域的方法学和计算平台开发，推进复杂疾病的精准医学研究。在Science Advances, Genome Biology，Nature Communications, Genome Medicine, Bioinformatics等发表学术论文，受邀在Trends in Molecular Medicine, Trends in Biotechnology, Trends in Pharmacological Science，WIREs Computational Molecular Science等发表综述和评述论文。获2017年第七届吴文俊人工智能自然科学技术奖三等奖、获2018年药明康德生命化学奖、入选2019年教育部“青年长江学者”。
第五届中国计算机学会生物信息学会议
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