Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration.

dc.contributor.author

McFarland, James M

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Ho, Zandra V

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Kugener, Guillaume

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Dempster, Joshua M

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Montgomery, Phillip G

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Bryan, Jordan G

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Krill-Burger, John M

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Green, Thomas M

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Vazquez, Francisca

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Boehm, Jesse S

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Golub, Todd R

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Hahn, William C

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Root, David E

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Tsherniak, Aviad

dc.date.accessioned

2021-02-24T21:02:07Z

dc.date.available

2021-02-24T21:02:07Z

dc.date.issued

2018-11-02

dc.date.updated

2021-02-24T21:02:06Z

dc.description.abstract

The availability of multiple datasets comprising genome-scale RNAi viability screens in hundreds of diverse cancer cell lines presents new opportunities for understanding cancer vulnerabilities. Integrated analyses of these data to assess differential dependency across genes and cell lines are challenging due to confounding factors such as batch effects and variable screen quality, as well as difficulty assessing gene dependency on an absolute scale. To address these issues, we incorporated cell line screen-quality parameters and hierarchical Bayesian inference into DEMETER2, an analytical framework for analyzing RNAi screens ( https://depmap.org/R2-D2 ). This model substantially improves estimates of gene dependency across a range of performance measures, including identification of gold-standard essential genes and agreement with CRISPR/Cas9-based viability screens. It also allows us to integrate information across three large RNAi screening datasets, providing a unified resource representing the most extensive compilation of cancer cell line genetic dependencies to date.

dc.identifier

10.1038/s41467-018-06916-5

dc.identifier.issn

2041-1723

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2041-1723

dc.identifier.uri

https://hdl.handle.net/10161/22389

dc.language

eng

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Springer Science and Business Media LLC

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Nature communications

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10.1038/s41467-018-06916-5

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Humans

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Neoplasms

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RNA Interference

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Genes, Essential

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Models, Genetic

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Software

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Genetic Testing

dc.title

Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration.

dc.type

Journal article

duke.contributor.orcid

Bryan, Jordan G|0000-0002-4984-0516

pubs.begin-page

4610

pubs.issue

1

pubs.organisational-group

Student

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Statistical Science

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Duke

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Trinity College of Arts & Sciences

pubs.publication-status

Published

pubs.volume

9

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