Eleven grand challenges in single-cell data science
David Lähnemann (Heinrich Heine University, University Hospital Essen, Helmholtz Centre for Infection Research (HZI))
Johannes Köster (Harvard Medical School, University Hospital Essen)
Mark D. Robinson (Universitat Zurich)
Catalina A. Vallejos (The University of Edinburgh, The Alan Turing Institute)
Kieran R. Campbell (BC Cancer Agency, University of British Columbia)
Niko Beerenwinkel (ETH Zürich, SIB Swiss Institute of Bioinformatics)
Luca Pinello (Massachusetts General Hospital, Broad Institute of MIT and Harvard, Harvard Medical School)
Boudewijn P.F. Lelieveldt (TU Delft - Electrical Engineering, Mathematics and Computer Science, Leiden University Medical Center)
Marcel Reinders (Leiden University Medical Center, TU Delft - Electrical Engineering, Mathematics and Computer Science)
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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.
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