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Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics
View ORCID ProfilePierre Boyeau, View ORCID ProfileJustin Hong, View ORCID ProfileAdam Gayoso, Michael I. Jordan, Elham Azizi, Nir Yosef
doi: https://doi.org/10.1101/2022.10.04.510898
Pierre Boyeau
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Justin Hong
2Department of Computer Science, Columbia University
6Irving Institute for Cancer Dynamics, Columbia University
Adam Gayoso
3Center for Computational Biology, University of California, Berkeley
Michael I. Jordan
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
3Center for Computational Biology, University of California, Berkeley
4Department of Statistics, University of California, Berkeley
Elham Azizi
2Department of Computer Science, Columbia University
5Department of Biomedical Engineering, Columbia University
6Irving Institute for Cancer Dynamics, Columbia University
Nir Yosef
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
3Center for Computational Biology, University of California, Berkeley
7Department of Systems Immunology, Weizmann Institute of Science
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Posted October 06, 2022.
Deep generative modeling for quantifying sample-level heterogeneity in single-cell omics
Pierre Boyeau, Justin Hong, Adam Gayoso, Michael I. Jordan, Elham Azizi, Nir Yosef
bioRxiv 2022.10.04.510898; doi: https://doi.org/10.1101/2022.10.04.510898
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