Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

Author:

Xu Chenling1ORCID,Lopez Romain2ORCID,Mehlman Edouard23ORCID,Regier Jeffrey4ORCID,Jordan Michael I25ORCID,Yosef Nir1267ORCID

Affiliation:

1. Center for Computational Biology University of California Berkeley CA USA

2. Department of Electrical Engineering and Computer Sciences University of California Berkeley CA USA

3. Centre de Mathématiques Appliquées École polytechnique Palaiseau France

4. Department of Statistics University of Michigan Ann Arbor MI USA

5. Department of Statistics University of California Berkeley CA USA

6. Ragon Institute of MGHMIT and Harvard Boston MA USA

7. Chan‐Zuckerberg Biohub Investigator San Francisco CA USA

Funder

Division of Intramural Research, National Institute of Allergy and Infectious Diseases

National Institute of Mental Health

Publisher

EMBO

Subject

Applied Mathematics,Computational Theory and Mathematics,General Agricultural and Biological Sciences,General Immunology and Microbiology,General Biochemistry, Genetics and Molecular Biology,Information Systems

Reference80 articles.

1. Magan: Aligning biological manifolds;Amodio M;Proc Int Conf Mach Learn,2018

2. Exploring single‐cell data with deep multitasking neural networks;Amodio M;Nat Meth,2019

3. M3Drop: dropout-based feature selection for scRNASeq

4. Single cells make big data: New challenges and opportunities in transcriptomics

5. A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

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