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Eleven grand challenges in single-cell data science

Research output: Contribution to journalReview article

  • David Lähnemann
  • Johannes Köster
  • Ewa Szczurek
  • Davis J McCarthy
  • Stephanie C Hicks
  • Mark D Robinson
  • Kieran R Campbell
  • Niko Beerenwinkel
  • Ahmed Mahfouz
  • Luca Pinello
  • Pavel Skums
  • Alexandros Stamatakis
  • Camille Stephan-Otto Attolini
  • Samuel Aparicio
  • Jasmijn Baaijens
  • Marleen Balvert
  • Buys de Barbanson
  • Antonio Cappuccio
  • Giacomo Corleone
  • Bas E Dutilh
  • Maria Florescu
  • Victor Guryev
  • Rens Holmer
  • Katharina Jahn
  • Thamar Jessurun Lobo
  • Emma M Keizer
  • Indu Khatri
  • Szymon M Kielbasa
  • Jan O Korbel
  • Alexey M Kozlov
  • Tzu-Hao Kuo
  • Boudewijn P F Lelieveldt
  • Ion I Mandoiu
  • John C Marioni
  • Tobias Marschall
  • Felix Mölder
  • Amir Niknejad
  • Lukasz Raczkowski
  • Marcel Reinders
  • Jeroen de Ridder
  • Antoine-Emmanuel Saliba
  • Antonios Somarakis
  • Oliver Stegle
  • Fabian J Theis
  • Huan Yang
  • Alex Zelikovsky
  • Alice C McHardy
  • Benjamin J Raphael
  • Sohrab P Shah
  • Alexander Schönhuth

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Original languageEnglish
Pages (from-to)31
JournalGenome Biology
Volume21
Issue number1
DOIs
Publication statusPublished - 7 Feb 2020

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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