Learning Differentiation Dynamics from Lineage Tracing Datasets

Explore a comprehensive lecture on learning differentiation dynamics from lineage tracing datasets presented by Shou-Wen Wang, a Damon Runyon Computational Biology Fellow at Harvard Medical School. Delve into the development of coherent, sparse optimization (CoSpar), a robust computational approach for inferring cell dynamics from single-cell transcriptomics integrated with lineage tracing. Discover how this method, related to compressed sensing in applied mathematics, overcomes challenges associated with noisy, dispersed lineage data. Examine the application of CoSpar in various biological contexts, including hematopoiesis, reprogramming, and directed differentiation, and learn how it identifies early fate biases and predicts transcription factors and receptors involved in fate choice. Gain insights into the method's assumptions, design, implementation, and limitations, as well as its potential to revolutionize our understanding of cell differentiation, disease onset, and drug response dynamics.

Introduction
Background
Learning differentiation dynamics
Assumptions
Coherence Organization
Image Reconstruction
Field Lasso
Challenges
Design
Implementation
Applications
Limitations