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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.

SUBMITTER: Lahnemann D

PROVIDER: S-EPMC7007675 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Eleven grand challenges in single-cell data science.

Lähnemann David D Köster Johannes J Szczurek Ewa E McCarthy Davis J DJ Hicks Stephanie C SC Robinson Mark D MD Vallejos Catalina A CA Campbell Kieran R KR Beerenwinkel Niko N Mahfouz Ahmed A Pinello Luca L Skums Pavel P Stamatakis Alexandros A Attolini Camille Stephan-Otto CS Aparicio Samuel S Baaijens Jasmijn J Balvert Marleen M Barbanson Buys de B Cappuccio Antonio A Corleone Giacomo G Dutilh Bas E BE Florescu Maria M Guryev Victor V Holmer Rens R Jahn Katharina K Lobo Thamar Jessurun TJ Keizer Emma M EM Khatri Indu I Kielbasa Szymon M SM Korbel Jan O JO Kozlov Alexey M AM Kuo Tzu-Hao TH Lelieveldt Boudewijn P F BPF Mandoiu Ion I II Marioni John C JC Marschall Tobias T Mölder Felix F Niknejad Amir A Rączkowska Alicja A Reinders Marcel M Ridder Jeroen de J Saliba Antoine-Emmanuel AE Somarakis Antonios A Stegle Oliver O Theis Fabian J FJ Yang Huan H Zelikovsky Alex A McHardy Alice C AC Raphael Benjamin J BJ Shah Sohrab P SP Schönhuth Alexander A

Genome biology 20200207 1

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 questio ...[more]

PMID: 32033589

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Project description:Highlights • Complex socio-technical challenges lack a method for their holistic framing.• Existent problem-framing methods are biased in favor of expert opinion.• CSFA provides a pattern-based ontological frame for complex problems.• System user data can be valuably mined to aid experts with problem-framing.• CSFA yields frames with improved coverage of plurality, abstraction, context and scope. Complex socio-technical challenges, often referred to as grand challenges or wicked problems, lack a robust method for their holistic framing. Current approaches to framing fall into two primary categories. On one hand, models grounded in reductionist perspectives tend to oversimplify the problems and thus fall short of capturing the true complexity that must be understood to make tangible progress. On the other, notable attempts to achieve holism are more effective at incorporating contextual nuance, but still lack systematicity to identify and drive effective inclusion of critical issues, and also tend to suffer from the inherent bias of select expert input. In this article, we report on an extension of holistic problem framing techniques called comprehensive success factor analysis (CSFA) that makes-sense of web-mined information reflective of both expert and general population perspectives as well as pattern-informed ontological knowledge organization structure, to yield ‘richer pictures’ of grand challenges. This method has been developed and refined over a seven-year period by application to a variety of distinct socio-technical challenges, and emphasizes that framing complex problems requires one to embrace multiple levels of abstraction, a plurality of perspectives, careful contextualization, and an overarching system view. The CSFA method results in ‘success factor trees’ that are more comprehensive than seen otherwise and present a holistic view of the essential factors that need to be considered when engaging in large scale socio-technical problems. The success factor trees provide common grounds for meaningful collaboration and discourse on grand challenges, facilitate more informed resource allocation decisions, and provide guidance for designing solutions through careful consideration of system factors that are not always apparent. The paper illustrates CSFA applied to the challenge of ‘food security for a nation in a low- to middle-income country context’ to ascertain the value of the approach and finds that it results in a robust view of the challenge that greatly exceeds perspectives arrived at in the literature using current framing methods, on dimensions of scope, levels of abstraction, plurality, and context detail. Graphical abstract Image, graphical abstract

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Eleven grand challenges in single-cell data science.

Publications

Eleven grand challenges in single-cell data science.

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