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

Journal Article (Journal Article)

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 ( ). 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.

Full Text

Duke Authors

Cited Authors

  • McFarland, 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

Published Date

  • November 2, 2018

Published In

Volume / Issue

  • 9 / 1

Start / End Page

  • 4610 -

PubMed ID

  • 30389920

Pubmed Central ID

  • PMC6214982

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

International Standard Serial Number (ISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-018-06916-5


  • eng