Eleven grand challenges in single-cell data science

Eleven grand challenges in single-cell data science

Lähnemann, David (University Hospital Essen; Heinrich Heine University; Helmholtz Centre for Infection Research (HZI))
Köster, Johannes (University Hospital Essen; Harvard Medical School)
Robinson, Mark D. (University of Zürich)
Vallejos, Catalina A. (The University of Edinburgh; The Alan Turing Institute)
Campbell, Kieran R. (University of British Columbia; BC Cancer Agency)
Beerenwinkel, Niko (ETH Zürich; SIB Swiss Institute of Bioinformatics)
Pinello, Luca (Massachusetts General Hospital; Harvard Medical School; Broad Institute of MIT and Harvard)
Lelieveldt, B.P.F. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)
Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center)

2020-02-07

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.

http://resolver.tudelft.nl/uuid:f16a7eba-308b-42d4-9f3a-b15080cc7aae

https://doi.org/10.1186/s13059-020-1926-6

1474-760X

Genome Biology (Online), 21 (1)

Institutional Repository

review

© 2020 David Lähnemann, Johannes Köster, Mark D. Robinson, Catalina A. Vallejos, Kieran R. Campbell, Niko Beerenwinkel, Luca Pinello, B.P.F. Lelieveldt, M.J.T. Reinders, More Authors