报告题目：Inferring developmental trajectories and causal regulations with single-cell measurements

报告人🏕：邱肖杰, 美国华盛顿大学博士后

主持人：翁杰敏 教授

报告时间🤸‍♂️：2018年9月5日（周三） 下午14:00—15:00

报告地点：天美娱乐534小会议室

 

报告人简介：邱肖杰🦧➙，2008年本科毕业于长春科技大学生物工程专业🫄🏼🟩，2012年在天美获得生物信息学硕士学位🛀🏽，2018年在华盛顿大学获得分子与细胞生物学专业博士学位，目前是美国华盛顿大学的博士后。邱博士一直从事计算生物学与单细胞基因组学的研究，已作为第一作者在Nature method杂志上发表论文2篇👰🏼‍♀️。

 

报告摘要：Development is commonly regarded as a hierarchical branching process. Single-cell genomics, single-cell RNA-seq (scRNA-seq) in particular, holds the promise to resolve the dynamics of this process. However, learning the structure of complex single-cell trajectories with multiple branches remains a challenging computational problem. In this seminar, I will present the toolkit, Monocle 2, which uses reversed graph embedding to reconstruct single-cell trajectories in a fully unsupervised manner. Monocle 2 learns an explicit “principal graph” that passes through the middle of the data as opposed to other ad hoc methods, greatly improving the robustness and accuracy of its trajectories. I will demonstrate that Monocle 2 is able to accurately reconstruct developmental trajectories for complicated systems, including haematopoiesis involving multiple different cell fates. I will also talk about recent developments of Monocle, Monocle 3, to analyze complex large cell atlas dataset with multiple developmental trajectories and loops.  When coupled with another statistical framework, BEAM (branch expression analysis modeling), Monocle 2 is able to detect genes specific to different developmental lineages. The unprecedented high resolution of the reconstructed developmental trajectories not only enables us to determine which genes are playing important roles at the critical time point of cell fate transition, but also to directly infer causal gene regulatory networks. To this end, I have been developing a new toolkit, Scribe, which applies novel information theory techniques to detect causal interactions responsible for fate transitions.