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Genome Biol. 2020 Feb 7;21(1):31. doi: 10.1186/s13059-020-1926-6.

Eleven grand challenges in single-cell data science.

Author information

1
Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
2
Department of Paediatric Oncology, Haematology and Immunology, Medical Faculty, Heinrich Heine University, University Hospital, Düsseldorf, Germany.
3
Computational Biology of Infection Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany.
4
Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, USA.
5
Institute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, Warszawa, Poland.
6
Bioinformatics and Cellular Genomics, St Vincent's Institute of Medical Research, Fitzroy, Australia.
7
Melbourne Integrative Genomics, School of BioSciences-School of Mathematics & Statistics, Faculty of Science, University of Melbourne, Melbourne, Australia.
8
Department of Biostatistics, Johns Hopkins University, Baltimore, MD, USA.
9
Institute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of Zürich, Zürich, Switzerland.
10
MRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, UK.
11
The Alan Turing Institute, British Library, London, UK.
12
Department of Statistics, University of British Columbia, Vancouver, Canada.
13
Department of Molecular Oncology, BC Cancer Agency, Vancouver, Canada.
14
Data Science Institute, University of British Columbia, Vancouver, Canada.
15
Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland.
16
SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland.
17
Leiden Computational Biology Center, Leiden University Medical Center, Leiden, The Netherlands.
18
Delft Bioinformatics Lab, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands.
19
Molecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research Institute, Charlestown, USA.
20
Department of Pathology, Harvard Medical School, Boston, USA.
21
Broad Institute of Harvard and MIT, Cambridge, MA, USA.
22
Department of Computer Science, Georgia State University, Atlanta, USA.
23
Computational Molecular Evolution Group, Heidelberg Institute for Theoretical Studies, Heidelberg, Germany.
24
Institute for Theoretical Informatics, Karlsruhe Institute of Technology, Karlsruhe, Germany.
25
Institute for Research in Biomedicine, The Barcelona Institute of Science and Technology, Barcelona, Spain.
26
Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, Canada.
27
Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands.
28
Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands.
29
Center for Molecular Medicine, University Medical Center Utrecht, Utrecht, The Netherlands.
30
Oncode Institute, Utrecht, The Netherlands.
31
Quantitative biology, Hubrecht Institute, Utrecht, The Netherlands.
32
Institute for Advanced Study, University of Amsterdam, Amsterdam, The Netherlands.
33
Department of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College London, London, UK.
34
Centre for Molecular and Biomolecular Informatics, Radboud University Medical Center, Nijmegen, The Netherlands.
35
European Research Institute for the Biology of Ageing, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands.
36
Bioinformatics Group, Wageningen University, Wageningen, The Netherlands.
37
Biometris, Wageningen University & Research, Wageningen, The Netherlands.
38
Department of Immunohematology and Blood Transfusion, Leiden University Medical Center, Leiden, The Netherlands.
39
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
40
Genome Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany.
41
PRB lab, Delft University of Technology, Delft, The Netherlands.
42
Division of Image Processing, Department of Radiology, Leiden University Medical Center, Leiden, The Netherlands.
43
Computer Science & Engineering Department, University of Connecticut, Storrs, USA.
44
Cancer Research UK Cambridge Institute, Li Ka Shing Centre, University of Cambridge, Cambridge, UK.
45
Wellcome Trust Sanger Institute, Wellcome Genome Campus, Hinxton, UK.
46
European Molecular Biology Laboratory, European Bioinformatics Institute, Hinxton, UK.
47
Center for Bioinformatics, Saarland University, Saarbrücken, Germany.
48
Max Planck Institute for Informatics, Saarbrücken, Germany.
49
Institute of Pathology, University Hospital Essen, University of Duisburg-Essen, Essen, Germany.
50
Computation molecular design, Zuse Institute Berlin, Berlin, Germany.
51
Mathematics Department, Mount Saint Vincent, New York, USA.
52
Helmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection Research, Würzburg, Germany.
53
Division of Computational Genomics and Systems Genetics, German Cancer Research Center-DKFZ, Heidelberg, Germany.
54
Institute of Computational Biology, Helmholtz Zentrum München-German Research Center for Environmental Health, Neuherberg, Germany.
55
Division of Drug Discovery and Safety, Leiden Academic Center for Drug Research-LACDR-Leiden University, Leiden, The Netherlands.
56
The Laboratory of Bioinformatics, I.M. Sechenov First Moscow State Medical University, Moscow, Russia.
57
Department of Computer Science, Princeton University, Princeton, USA.
58
Computational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, USA.
59
Life Sciences and Health, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands. as@cwi.nl.
60
Theoretical Biology and Bioinformatics, Science for Life, Utrecht University, Utrecht, The Netherlands. as@cwi.nl.

Abstract

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

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