Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions
Name
s41467-021-23518-w.pdf
Description
Published version
Size
3.39 MB
Format
Adobe PDF
Checksum (MD5)
f60a61a34d0d18733e9c1421a42b9fb7
Author(s)
Yeo, Grace Hui Ting
Saksena, Sachit D
Gifford, David K
Date Issued
2021
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Yeo, Grace Hui Ting, Saksena, Sachit D and Gifford, David K. 2021. "Generative modeling of single-cell time series with PRESCIENT enables prediction of cell trajectories with interventions." Nature Communications, 12 (1).
Version
Final published version
Abstract
Abstract
Existing computational methods that use single-cell RNA-sequencing (scRNA-seq) for cell fate prediction do not model how cells evolve stochastically and in physical time, nor can they predict how differentiation trajectories are altered by proposed interventions. We introduce PRESCIENT (Potential eneRgy undErlying Single Cell gradIENTs), a generative modeling framework that learns an underlying differentiation landscape from time-series scRNA-seq data. We validate PRESCIENT on an experimental lineage tracing dataset, where we show that PRESCIENT is able to predict the fate biases of progenitor cells in hematopoiesis when accounting for cell proliferation, improving upon the best-performing existing method. We demonstrate how PRESCIENT can simulate trajectories for perturbed cells, recovering the expected effects of known modulators of cell fate in hematopoiesis and pancreatic β cell differentiation. PRESCIENT is able to accommodate complex perturbations of multiple genes, at different time points and from different starting cell populations, and is available at t https://
github.com/gifford-lab/prescient.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Computational and Systems Biology Program
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41467-021-23518-W
