Eleven grand challenges in single-cell data science | Genome Biology | Full Text

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.

Eleven grand challenges in single-cell data science Review Open access Published: 07 February 2020 Volume 21 , article number 31 ( 2020 ) Cite this article You have full access to this open access article Download PDF Save article View saved research Genome Biology Aims and scope Submit manuscript Eleven grand challenges in single-cell data science Download PDF 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 am

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