Joint Biology/Computational Biology Faculty Candidate

  • Mellon Institute
  • Mellon Conference Room
  • Ph.D. Candidate
  • Molecular and Cellular Biology
  • University of Washington

Inferring developmental trajectories and causal regulations with single-cell genomics

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. 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. In my future lab, I envision building upon my foundational work on scRNA-seq analysis to comprehensively map cellular lineages and the corresponding regulatory hierarchies in systems like sea urchin.

Xiaojie Qiu was raised in a small village of southern China. He then attended Changchun University of Technology where he completed an undergraduate degree in bioengineering. Afterwards he earned a Masters in bioinformatics from East China Normal University in Shanghai. During his Masters, he applied dynamic systems approaches to understand the irreversibility of cell fate transitions. After a brief stint with Dr. Sui Huang, working on simulating evolution of developmental regulatory networks, at the Institute for Systems Biology (Seattle), Xiaojie started his PhD in the Molecular and Cellular Biology program at the University of Washington. Excited by the promise of single-cell genomics, Xiaojie joined Dr. Cole Trapnell’s lab in the department of Genome Sciences as his first graduate student, to develop computational methods for single-cell genomics. Xiaojie’s PhD work has made a few key contributions to the field of single-cell genomics. For example, he developed the popular single-cell genomics analysis toolkit, Monocle 2, to accurately and robustly reconstruct complex developmental trajectories. He also proposed BEAM (branch expression analysis modeling), a statistical framework that identifies genes significantly diverge between different lineages and pinpoints the precise timing of lineage specification events. Recently, in close collaboration with Dr. Sreeram Kannan, he has been developing and applying information theory techniques to detect casual interactions responsible for cell fate decisions with single-cell genomics datasets.

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