Journal
NATURE COMMUNICATIONS
Volume 11, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41467-020-17569-8
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Funding
- March of Dimes Prematurity Research Center at Stanford
- Bill and Melinda Gates Foundation [OPP1112382, OPP1113682]
- Department of Anesthesiology, Perioperative and Pain Medicine at Stanford University
- Burroughs Wellcome Fund
- National Institute of Health [KL2TR003143, K23GM111657, R21DE02772801, R01AG058417, R01HL13984401, R61NS114926]
- U.S. FDA [HHSF223201610018C]
- Stanford Immunology Training Grant [5T32AI07290-33]
- Stanford Anesthesia Training Grant [T32GM089626]
- German Research Foundation (DFG) [STE 2757/1-1]
- Doris Duke Charitable Foundation
- Stanford Maternal and Child Health Research Institute
- Charles and Mary Robertson Foundation
- American Heart Association [19PABHI34580007]
- Bill and Melinda Gates Foundation [OPP1112382] Funding Source: Bill and Melinda Gates Foundation
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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. 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.
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