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Explore a 47-minute talk by Maria Yampolskaya from Valence Labs discussing the innovative "single-cell Type Order Parameters" (scTOP) approach for analyzing single-cell RNA sequencing data. Learn about this physics-inspired methodology that quantifies cell identity using reference cell types without requiring feature selection, statistical fitting, or dimensional reduction techniques. Discover how scTOP accurately classifies cells, visualizes developmental trajectories, and assesses engineered cell fidelity across human and mouse datasets. The presentation highlights specific applications including characterization of hybrid alveolar cell populations in mouse lungs, visualization of hematopoiesis lineage tracing, and evaluation of transcriptional similarity between endogenous and donor-derived cells in pulmonary cell transplantation. This talk is part of the Multiomics Reading Group hosted by Portal, the AI for drug discovery community platform, and examines the paper published in Development journal that introduces scTOP as an accessible Python package for understanding differentiation and characterizing engineered cells.
