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VoPo leverages cellular heterogeneity for predictive modeling of single-cell data

Authors :
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
Source :
Nature Communications, Vol 11, Iss 1, Pp 1-9 (2020)
Publication Year :
2020
Publisher :
Nature Portfolio, 2020.

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.

Subjects

Subjects :
Science

Details

Language :
English
ISSN :
20411723
Volume :
11
Issue :
1
Database :
Directory of Open Access Journals
Journal :
Nature Communications
Publication Type :
Academic Journal
Accession number :
edsdoj.63eb4be164c423694059f87cd18e59d
Document Type :
article
Full Text :
https://doi.org/10.1038/s41467-020-17569-8