Eleven grand challenges in single-cell data science

Laehnemann D, Koester J, Szczurek E, Mccarthy DJ, Hicks SC, Robinson MD, Vallejos CA, Campbell KR, Beerenwinkel N, Mahfouz A, Pinello L, Skums P, Stamatakis A, Attolini CSO, Aparicio S, Baaijens J, Balvert M, De Barbanson B, Cappuccio A, Corleone G, Dutilh BE, Florescu M, Guryev V, Holmer R, Jahn K, Lobo TJ, Keizer EM, Khatri I, Kielbasa SM, Korbel JO, Kozlov AM, Kuo TH, Lelieveldt BPF, Mandoiu II, Marioni JC, Marschall T, Moelder F, Niknejad A, Raczkowski L, Reinders M, De Ridder J, Saliba AE, Somarakis A, Stegle O, Theis FJ, Yang H, Zelikovsky A, Mchardy AC, Raphael BJ, Shah SP, Schonhuth A (2020)

Publication Type: Journal article, Review article

Publication year: 2020

Journal

Book Volume: 21

Article Number: 31

Journal Issue: 1

DOI: 10.1186/s13059-020-1926-6

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.

Involved external institutions

University Medical Centre Utrecht (UMC Utrecht) Netherlands (NL) Helmholtz-Zentrum für Infektionsforschung (HZI) Germany (DE) Leiden University Netherlands (NL) Universität Duisburg-Essen (UDE) Germany (DE) University of Warsaw / Uniwersytet Warszawski Poland (PL) St Vincents Institute of Medical Research (SVI) Australia (AU) Johns Hopkins University (JHU) United States (USA) (US) University of Zurich / Universität Zürich (UZH) Switzerland (CH) University of Edinburgh United Kingdom (GB) University of British Columbia Canada (CA) Eidgenössische Technische Hochschule Zürich (ETHZ) / Swiss Federal Institute of Technology in Zurich Switzerland (CH) Massachusetts General Hospital United States (USA) (US) Georgia State University United States (USA) (US) Heidelberg Institute for Theoretical Studies GmbH (HITS) Germany (DE) Autonomous University of Barcelona (UAB) / Universitat Autònoma de Barcelona Spain (ES) British Columbia Cancer Agency Canada (CA) Centrum Wiskunde & Informatica (CWI) Netherlands (NL) University of Amsterdam Netherlands (NL) Imperial College London / The Imperial College of Science, Technology and Medicine United Kingdom (GB) Universiteit Utrecht (UU) / Utrecht University Netherlands (NL) University of Groningen / Rijksuniversiteit Groningen Netherlands (NL) Wageningen University & Research Netherlands (NL) European Molecular Biology Laboratory (EMBL) Germany (DE) Delft University of Technology (TU Delft) Netherlands (NL) University of Connecticut United States (USA) (US) University of Cambridge United Kingdom (GB) Universität des Saarlandes (UdS) Germany (DE) Konrad-Zuse-Zentrum für Informationstechnik / Zuse Institute Berlin (ZIB) Germany (DE) Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt (HMGU) / Helmholtz Munich Germany (DE) Princeton University United States (USA) (US) Memorial Sloan Kettering Cancer Center United States (USA) (US)

How to cite

APA:

Laehnemann, D., Koester, J., Szczurek, E., Mccarthy, D.J., Hicks, S.C., Robinson, M.D.,... Schonhuth, A. (2020). Eleven grand challenges in single-cell data science. Genome Biology, 21(1). https://doi.org/10.1186/s13059-020-1926-6

MLA:

Laehnemann, David, et al. "Eleven grand challenges in single-cell data science." Genome Biology 21.1 (2020).

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