Eleven grand challenges in single-cell data science Review


Authors: 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.; de Barbanson, B.; 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.; de Ridder, J.; 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.
Review Title: 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 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. © 2020 The Author(s).
Keywords: review; human; data science
Journal Title: Genome Biology
Volume: 21
ISSN: 1465-6906
Publisher: Biomed Central Ltd  
Date Published: 2020-01-01
Start Page: 31
Language: English
DOI: 10.1186/s13059-020-1926-6
PUBMED: 32033589
PROVIDER: scopus
PMCID: PMC7007675
DOI/URL:
Notes: Review -- Export Date: 2 March 2020 -- Source: Scopus
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  1. Sohrab Prakash Shah
    86 Shah