Inferring Developmental Trajectories and Causal Regulations with Single-cell Genomics

Abstract

Development is commonly regarded as a hierarchical branching process which is governed by underlying gene regulatory networks. 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 thesis, 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 hematopoiesis 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. Scribe provides intuitive visualizations of causal interactions and can additionally incorporate information from “RNA-velocity” for causality detection. Scribe accurately reconstructs core networks specifying myelocytic or chromaffin cells. Finally, I will show a compendium of the inferred causal regulatory network for C elegans’ early embryogenesis based on lineage resolved live imaging data, demonstrating Scribe’s generalizability.