Massive single-cell profiling efforts have accelerated our discovery of the cellular composition of the human body, while at the same time raising the need to formalise this new knowledge.
While there is no analogous ground truth for real life biological systems, this work demonstrates the ability to construct and parameterize a considerable diversity of network models with high predictive ability.
no code implementations • 18 Mar 2019 • Michael P Snyder, Shin Lin, Amanda Posgai, Mark Atkinson, Aviv Regev, Jennifer Rood, Orit Rosen, Leslie Gaffney, Anna Hupalowska, Rahul Satija, Nils Gehlenborg, Jay Shendure, Julia Laskin, Pehr Harbury, Nicholas A Nystrom, Ziv Bar-Joseph, Kun Zhang, Katy Börner, Yiing Lin, Richard Conroy, Dena Procaccini, Ananda L Roy, Ajay Pillai, Marishka Brown, Zorina S Galis
Transformative technologies are enabling the construction of three dimensional (3D) maps of tissues with unprecedented spatial and molecular resolution.
Specifically, we extend the framework of the classical Dirichlet diffusion tree to simultaneously infer branch topology and latent cell states along continuous trajectories over the full tree.