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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.d.* ; Cappuccio, A.* ; Corleone, G.* ; Dutilh, B.E.* ; Florescu, M.* ; Guryev, V.* ; Holmer, R.* ; Jahn, K.* ; Lobo, T.J.* ; Keizer, E.M.* ; Khatri, I.* ; Kielbasa, S.M.* ; Korbel, J.O.* ; Kozlov, A.M.* ; Kuo, T.H.* ; Lelieveldt, B.P.F.* ; Mandoiu, I.I.* ; Marioni, J.C.* ; Marschall, T.* ; Mölder, F.* ; Niknejad, A.* ; Raczkowski, L.* ; Reinders, M.* ; Ridder, J.d.* ; Saliba, A.E.* ; Somarakis, A.* ; Stegle, O.* ; Theis, F.J. ; Yang, H.* ; Zelikovsky, A.* ; McHardy, A.C.* ; Raphael, B.J.* ; Shah, S.P.* ; Schönhuth, A.*

Eleven grand challenges in single-cell data science.

Genome Biol. 21:31 (2020)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
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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Publication type Article: Journal article
Document type Review
Corresponding Author
ISSN (print) / ISBN 1474-760X
e-ISSN 1465-6906
Journal Genome Biology
Quellenangaben Volume: 21, Issue: 1, Pages: , Article Number: 31 Supplement: ,
Publisher BioMed Central
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)