Abstract:
With fast advancement of bio-technology in single-cell/spatial transcriptomics, a large number of computational methods have been developed to tackle various biological questions. Here, we present developed computational methods from static modeling, such as clustering, embedding alignment, co-embedding, etc., to dynamic modeling. In our recent study, we present SDEvelo, a generative approach to inferring RNA velocity by modeling the dynamics of unspliced and spliced RNAs via multivariate stochastic differential equations (SDE). Using both simulated and four scRNA-seq and spatial transcriptomics datasets, where we show that SDEvelo can model the random dynamic patterns of mature-state cells while accurately detecting carcinogenesis.
About the Speaker:
刘瑾博士，香港中文大学（深圳）数据科学学院副教授、校长学者，国际统计协会（ISI）当选会士，中国卒中学会多组学分会委员。曾任新加坡国立大学Duke-NUS医学院助理教授。主要参与统计计算及其在统计遗传/基因组学中的应用研究，当前研究方向包括单细胞与空间转录组数据整合研究、全转录组关联分析、孟德尔随机。作为第一/通信作者把相关系列研究发表在Annals of Statistics, Nature Communications, Nucleic Acids Research, Gut, Briefings in Bioinformatics, Bioinformatics, Biometrics, Biostatistics, IEEE Transactions on Information Theory等杂志。四次主持新加坡教育部颁发的学术研究基金（AcRF Tier 2），主持国家自然科学基金项目，参与广东省“珠江人才计划”引进创新创业团队项目。
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