Nature Communications (Jul 2020)
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data
- Natalie Stanley,
- Ina A. Stelzer,
- Amy S. Tsai,
- Ramin Fallahzadeh,
- Edward Ganio,
- Martin Becker,
- Thanaphong Phongpreecha,
- Huda Nassar,
- Sajjad Ghaemi,
- Ivana Maric,
- Anthony Culos,
- Alan L. Chang,
- Maria Xenochristou,
- Xiaoyuan Han,
- Camilo Espinosa,
- Kristen Rumer,
- Laura Peterson,
- Franck Verdonk,
- Dyani Gaudilliere,
- Eileen Tsai,
- Dorien Feyaerts,
- Jakob Einhaus,
- Kazuo Ando,
- Ronald J. Wong,
- Gerlinde Obermoser,
- Gary M. Shaw,
- David K. Stevenson,
- Martin S. Angst,
- Brice Gaudilliere,
- Nima Aghaeepour
Affiliations
- Natalie Stanley
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Ina A. Stelzer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Amy S. Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Ramin Fallahzadeh
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Edward Ganio
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Martin Becker
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Thanaphong Phongpreecha
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Huda Nassar
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Sajjad Ghaemi
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Ivana Maric
- Department of Pediatrics, Stanford University
- Anthony Culos
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Alan L. Chang
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Maria Xenochristou
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Xiaoyuan Han
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Camilo Espinosa
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Kristen Rumer
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Laura Peterson
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Franck Verdonk
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Dyani Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Eileen Tsai
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Dorien Feyaerts
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Jakob Einhaus
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Kazuo Ando
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Ronald J. Wong
- Department of Pediatrics, Stanford University
- Gerlinde Obermoser
- Center for Human Systems Immunology, Stanford University
- Gary M. Shaw
- Department of Pediatrics, Stanford University
- David K. Stevenson
- Department of Pediatrics, Stanford University
- Martin S. Angst
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Brice Gaudilliere
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- Nima Aghaeepour
- Department of Anesthesiology, Perioperative and Pain Medicine, Stanford University
- DOI
- https://doi.org/10.1038/s41467-020-17569-8
- Journal volume & issue
-
Vol. 11,
no. 1
pp. 1 – 9
Abstract
Single-cell technologies are increasingly prominent in clinical applications, but predictive modelling with such data in large cohorts has remained computationally challenging. We developed a new algorithm, ‘VoPo’, for predictive modelling and visualization of single cell data for translational applications.
