Eleven grand challenges in single-cell data science
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Published:2020-02-07
Issue:1
Volume:21
Page:
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ISSN:1474-760X
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Container-title:Genome Biology
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language:en
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Short-container-title:Genome Biol
Author:
Lähnemann David, Köster Johannes, Szczurek Ewa, McCarthy Davis J., Hicks Stephanie C., Robinson Mark D.ORCID, Vallejos Catalina A., Campbell Kieran R., Beerenwinkel Niko, Mahfouz Ahmed, Pinello Luca, Skums Pavel, Stamatakis Alexandros, Attolini Camille Stephan-Otto, Aparicio Samuel, Baaijens Jasmijn, Balvert Marleen, Barbanson Buys de, Cappuccio Antonio, Corleone Giacomo, Dutilh Bas E., Florescu Maria, Guryev Victor, Holmer Rens, Jahn Katharina, Lobo Thamar Jessurun, Keizer Emma M., Khatri Indu, Kielbasa Szymon M., Korbel Jan O., Kozlov Alexey M., Kuo Tzu-Hao, Lelieveldt Boudewijn P.F., Mandoiu Ion I., Marioni John C., Marschall Tobias, Mölder Felix, Niknejad Amir, Raczkowski Lukasz, Reinders Marcel, Ridder Jeroen de, Saliba Antoine-Emmanuel, Somarakis Antonios, Stegle Oliver, Theis Fabian J., Yang Huan, Zelikovsky Alex, McHardy Alice C., Raphael Benjamin J., Shah Sohrab P., Schönhuth Alexander
Abstract
AbstractThe 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.
Publisher
Springer Science and Business Media LLC
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