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Nat Commun DOI:10.1038/s41467-018-06916-5

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

Publication TypeJournal Article
Year of Publication2018
AuthorsMcFarland, JM, Ho, ZV, Kugener, G, Dempster, JM, Montgomery, PG, Bryan, JG, Krill-Burger, JM, Green, TM, Vazquez, F, Boehm, JS, Golub, TR, Hahn, WC, Root, DE, Tsherniak, A
JournalNat Commun
Volume9
Issue1
Pages4610
Date Published2018 Nov 02
ISSN2041-1723
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.

DOI10.1038/s41467-018-06916-5
Pubmed

http://www.ncbi.nlm.nih.gov/pubmed/30389920?dopt=Abstract

Alternate JournalNat Commun
PubMed ID30389920
PubMed Central IDPMC6214982