Department
Biomedical Sciences
Document Type
Article
Publication Title
Nature Communications
ISSN
2041-1723
Volume
11
Issue
1
DOI
10.1038/s41467-020-17569-8
Publication Date
12-1-2020
Abstract
High-throughput single-cell analysis technologies produce an abundance of data that is critical for profiling the heterogeneity of cellular systems. We introduce VoPo (https://github.com/stanleyn/VoPo), a machine learning algorithm for predictive modeling and comprehensive visualization of the heterogeneity captured in large single-cell datasets. In three mass cytometry datasets, with the largest measuring hundreds of millions of cells over hundreds of samples, VoPo defines phenotypically and functionally homogeneous cell populations. VoPo further outperforms state-of-the-art machine learning algorithms in classification tasks, and identified immune-correlates of clinically-relevant parameters.
Recommended Citation
Stanley, N.,
Stelzer, I. A.,
Tsai, A. S.,
Fallahzadeh, R.,
Ganio, E. A.,
Becker, M.,
Phongpreecha, T.,
Nassar, H.,
Ghaemi, S.,
Maric, I.,
Culos, A.,
Chang, A. L.,
Xenochristou, M.,
Han, X.,
Espinosa, C.,
Rumer, K.,
Peterson, L.,
Verdonk, F.,
Gaudilliere, D.,
Tsai, E.,
Feyaerts, D.,
Einhaus, J.,
Ando, K.,
Wong, R. J.,
Obermoser, G.,
Shaw, G. M.,
Stevenson, D. K.,
Angst, M. S.,
&
Gaudilliere, B.
(2020).
VoPo leverages cellular heterogeneity for predictive modeling of single-cell data.
Nature Communications, 11(1),
DOI: 10.1038/s41467-020-17569-8
https://scholarlycommons.pacific.edu/dugoni-facarticles/715
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
