KIT | KIT-Bibliothek | Impressum | Datenschutz

Eleven grand challenges in single-cell data science

Lähnemann, D.; Köster, J.; Szczurek, E.; McCarthy, D. J.; Hicks, S. C.; Robinson, M. D.; Vallejos, C. A.; Campbell, K. R.; Beerenwinkel, N.; Mahfouz, A.; Pinello, L.; Skums, P.; Stamatakis, A.; Attolini, C. S.-O.; Aparicio, S.; Baaijens, J.; Balvert, M.; Barbanson, B.; Cappuccio, A.; Corleone, G.; ... mehr

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.

Open Access Logo


Verlagsausgabe §
DOI: 10.5445/IR/1000105857
Veröffentlicht am 24.02.2020
Originalveröffentlichung
DOI: 10.1186/s13059-020-1926-6
Scopus
Zitationen: 1
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Theoretische Informatik (ITI)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1465-6906, 1465-6914, 1474-7596, 1474-760X
KITopen-ID: 1000105857
Erschienen in Genome biology
Band 21
Heft 1
Seiten Article: 31
Nachgewiesen in Web of Science
Scopus
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page