Eleven grand challenges in single-cell data science
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...
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| Language: | English |
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BMC
2020-02-01
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| Series: | Genome Biology |
| Online Access: | https://doi.org/10.1186/s13059-020-1926-6 |
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DOAJ |
| language |
English |
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Article |
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DOAJ |
| author |
David Lähnemann Johannes Köster Ewa Szczurek Davis J. McCarthy Stephanie C. Hicks Mark D. Robinson Catalina A. Vallejos Kieran R. Campbell Niko Beerenwinkel Ahmed Mahfouz Luca Pinello Pavel Skums Alexandros Stamatakis Camille Stephan-Otto Attolini Samuel Aparicio Jasmijn Baaijens Marleen Balvert Buys de Barbanson Antonio Cappuccio Giacomo Corleone Bas E. Dutilh Maria Florescu Victor Guryev Rens Holmer Katharina Jahn Thamar Jessurun Lobo Emma M. Keizer Indu Khatri Szymon M. Kielbasa Jan O. Korbel Alexey M. Kozlov Tzu-Hao Kuo Boudewijn P.F. Lelieveldt Ion I. Mandoiu John C. Marioni Tobias Marschall Felix Mölder Amir Niknejad Lukasz Raczkowski Marcel Reinders Jeroen de Ridder Antoine-Emmanuel Saliba Antonios Somarakis Oliver Stegle Fabian J. Theis Huan Yang Alex Zelikovsky Alice C. McHardy Benjamin J. Raphael Sohrab P. Shah Alexander Schönhuth |
| spellingShingle |
David Lähnemann Johannes Köster Ewa Szczurek Davis J. McCarthy Stephanie C. Hicks Mark D. Robinson Catalina A. Vallejos Kieran R. Campbell Niko Beerenwinkel Ahmed Mahfouz Luca Pinello Pavel Skums Alexandros Stamatakis Camille Stephan-Otto Attolini Samuel Aparicio Jasmijn Baaijens Marleen Balvert Buys de Barbanson Antonio Cappuccio Giacomo Corleone Bas E. Dutilh Maria Florescu Victor Guryev Rens Holmer Katharina Jahn Thamar Jessurun Lobo Emma M. Keizer Indu Khatri Szymon M. Kielbasa Jan O. Korbel Alexey M. Kozlov Tzu-Hao Kuo Boudewijn P.F. Lelieveldt Ion I. Mandoiu John C. Marioni Tobias Marschall Felix Mölder Amir Niknejad Lukasz Raczkowski Marcel Reinders Jeroen de Ridder Antoine-Emmanuel Saliba Antonios Somarakis Oliver Stegle Fabian J. Theis Huan Yang Alex Zelikovsky Alice C. McHardy Benjamin J. Raphael Sohrab P. Shah Alexander Schönhuth Eleven grand challenges in single-cell data science Genome Biology |
| author_facet |
David Lähnemann Johannes Köster Ewa Szczurek Davis J. McCarthy Stephanie C. Hicks Mark D. Robinson Catalina A. Vallejos Kieran R. Campbell Niko Beerenwinkel Ahmed Mahfouz Luca Pinello Pavel Skums Alexandros Stamatakis Camille Stephan-Otto Attolini Samuel Aparicio Jasmijn Baaijens Marleen Balvert Buys de Barbanson Antonio Cappuccio Giacomo Corleone Bas E. Dutilh Maria Florescu Victor Guryev Rens Holmer Katharina Jahn Thamar Jessurun Lobo Emma M. Keizer Indu Khatri Szymon M. Kielbasa Jan O. Korbel Alexey M. Kozlov Tzu-Hao Kuo Boudewijn P.F. Lelieveldt Ion I. Mandoiu John C. Marioni Tobias Marschall Felix Mölder Amir Niknejad Lukasz Raczkowski Marcel Reinders Jeroen de Ridder Antoine-Emmanuel Saliba Antonios Somarakis Oliver Stegle Fabian J. Theis Huan Yang Alex Zelikovsky Alice C. McHardy Benjamin J. Raphael Sohrab P. Shah Alexander Schönhuth |
| author_sort |
David Lähnemann |
| title |
Eleven grand challenges in single-cell data science |
| title_short |
Eleven grand challenges in single-cell data science |
| title_full |
Eleven grand challenges in single-cell data science |
| title_fullStr |
Eleven grand challenges in single-cell data science |
| title_full_unstemmed |
Eleven grand challenges in single-cell data science |
| title_sort |
eleven grand challenges in single-cell data science |
| publisher |
BMC |
| series |
Genome Biology |
| issn |
1474-760X |
| publishDate |
2020-02-01 |
| description |
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. |
| url |
https://doi.org/10.1186/s13059-020-1926-6 |
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doaj-9368dc7f34b5489abc7fc8743d7a022d2021-02-07T12:49:21ZengBMCGenome Biology1474-760X2020-02-0121113510.1186/s13059-020-1926-6Eleven grand challenges in single-cell data scienceDavid Lähnemann0Johannes Köster1Ewa Szczurek2Davis J. McCarthy3Stephanie C. Hicks4Mark D. Robinson5Catalina A. Vallejos6Kieran R. Campbell7Niko Beerenwinkel8Ahmed Mahfouz9Luca Pinello10Pavel Skums11Alexandros Stamatakis12Camille Stephan-Otto Attolini13Samuel Aparicio14Jasmijn Baaijens15Marleen Balvert16Buys de Barbanson17Antonio Cappuccio18Giacomo Corleone19Bas E. Dutilh20Maria Florescu21Victor Guryev22Rens Holmer23Katharina Jahn24Thamar Jessurun Lobo25Emma M. Keizer26Indu Khatri27Szymon M. Kielbasa28Jan O. Korbel29Alexey M. Kozlov30Tzu-Hao Kuo31Boudewijn P.F. Lelieveldt32Ion I. Mandoiu33John C. Marioni34Tobias Marschall35Felix Mölder36Amir Niknejad37Lukasz Raczkowski38Marcel Reinders39Jeroen de Ridder40Antoine-Emmanuel Saliba41Antonios Somarakis42Oliver Stegle43Fabian J. Theis44Huan Yang45Alex Zelikovsky46Alice C. McHardy47Benjamin J. Raphael48Sohrab P. Shah49Alexander Schönhuth50Algorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-EssenAlgorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-EssenInstitute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of WarsawBioinformatics and Cellular Genomics, St Vincent’s Institute of Medical ResearchDepartment of Biostatistics, Johns Hopkins UniversityInstitute of Molecular Life Sciences and SIB Swiss Institute of Bioinformatics, University of ZürichMRC Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Western General HospitalDepartment of Statistics, University of British ColumbiaDepartment of Biosystems Science and Engineering, ETH ZurichLeiden Computational Biology Center, Leiden University Medical CenterMolecular Pathology Unit and Center for Cancer Research, Massachusetts General Hospital Research InstituteDepartment of Computer Science, Georgia State UniversityComputational Molecular Evolution Group, Heidelberg Institute for Theoretical StudiesInstitute for Research in Biomedicine, The Barcelona Institute of Science and TechnologyDepartment of Molecular Oncology, BC Cancer AgencyLife Sciences and Health, Centrum Wiskunde & InformaticaLife Sciences and Health, Centrum Wiskunde & InformaticaCenter for Molecular Medicine, University Medical Center UtrechtInstitute for Advanced Study, University of AmsterdamDepartment of Surgery and Cancer, The Imperial Centre for Translational and Experimental Medicine, Imperial College LondonTheoretical Biology and Bioinformatics, Science for Life, Utrecht UniversityCenter for Molecular Medicine, University Medical Center UtrechtEuropean Research Institute for the Biology of Ageing, University Medical Center Groningen, University of GroningenBioinformatics Group, Wageningen UniversityDepartment of Biosystems Science and Engineering, ETH ZurichEuropean Research Institute for the Biology of Ageing, University Medical Center Groningen, University of GroningenBiometris, Wageningen University & ResearchDepartment of Immunohematology and Blood Transfusion, Leiden University Medical CenterDepartment of Biomedical Data Sciences, Leiden University Medical CenterGenome Biology Unit, European Molecular Biology LaboratoryComputational Molecular Evolution Group, Heidelberg Institute for Theoretical StudiesComputational Biology of Infection Research Group, Helmholtz Centre for Infection ResearchPRB lab, Delft University of TechnologyComputer Science & Engineering Department, University of ConnecticutCancer Research UK Cambridge Institute, Li Ka Shing Centre, University of CambridgeCenter for Bioinformatics, Saarland UniversityAlgorithms for Reproducible Bioinformatics, Genome Informatics, Institute of Human Genetics, University Hospital Essen, University of Duisburg-EssenComputation molecular design, Zuse Institute BerlinInstitute of Informatics, Faculty of Mathematics, Informatics and Mechanics, University of WarsawLeiden Computational Biology Center, Leiden University Medical CenterCenter for Molecular Medicine, University Medical Center UtrechtHelmholtz Institute for RNA-based Infection Research, Helmholtz-Center for Infection ResearchDivision of Image Processing, Department of Radiology, Leiden University Medical CenterGenome Biology Unit, European Molecular Biology LaboratoryInstitute of Computational Biology, Helmholtz Zentrum München–German Research Center for Environmental HealthDivision of Drug Discovery and Safety, Leiden Academic Center for Drug Research–LACDR–Leiden UniversityDepartment of Computer Science, Georgia State UniversityComputational Biology of Infection Research Group, Helmholtz Centre for Infection ResearchDepartment of Computer Science, Princeton UniversityComputational Oncology, Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer CenterLife Sciences and Health, Centrum Wiskunde & InformaticaAbstract 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.https://doi.org/10.1186/s13059-020-1926-6 |