2018
DOI: 10.1038/s41467-018-06916-5
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Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

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-qu… Show more

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Cited by 321 publications
(325 citation statements)
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“…The loadings on the first principal component, which again explained most of the variance in olfactory receptor scores, were highly correlated with the variance in effect size estimates for olfactory receptor genes for each cell line ( R = −0.89, Fig EV1C). One possibility is that regressing on the variance of each cell line acts as a technical correction, re‐centering and scaling the effect sizes in a manner similar to that performed in a similar study (Rauscher et al , ) and recommended in a recent article (McFarland et al , ). In this case, our scaling would roughly equalize variance in biological effect sizes among negative control genes.…”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…The loadings on the first principal component, which again explained most of the variance in olfactory receptor scores, were highly correlated with the variance in effect size estimates for olfactory receptor genes for each cell line ( R = −0.89, Fig EV1C). One possibility is that regressing on the variance of each cell line acts as a technical correction, re‐centering and scaling the effect sizes in a manner similar to that performed in a similar study (Rauscher et al , ) and recommended in a recent article (McFarland et al , ). In this case, our scaling would roughly equalize variance in biological effect sizes among negative control genes.…”
Section: Resultsmentioning
confidence: 99%
“…Recent work has shown that copy number variation can underlie the strongest hits in CRISPR‐knockout screens, and multiple groups have proposed corrective algorithms to confront this problem (Pommier, ; Meyers et al , ; Data ref: Meyers et al , ; preprint: Wu et al , ). Additional heuristics aimed at increasing the quality of genetic interactions identified from parallel genetic screens have included discarding entire screens with noisy effect sizes, setting an effect size threshold for correlating genes, and capping the number of interactions per gene (Wang et al , ; preprint: Kim et al , ; Pan et al , ); however, reliance on these heuristics prevents truly unbiased genome‐wide analyses (McFarland et al , ). Furthermore, as the scale and diversity of published genetic screens grow, so will the need for new statistical techniques that can correct for technical variation while preserving even small levels of true signal.…”
Section: Introductionmentioning
confidence: 99%
“…RNAi viability data The RNAi viability data were downloaded from the Cancer Dependency Map portal (https://depmap.org/portal/download/). The "gene means proc" scores from the 19Q2 data were used, which were estimated using DEMETER2 [15,6]. The sign of the scores were flipped to match the directionality of the drug sensitivity data.…”
Section: Methodsmentioning
confidence: 99%
“…The fourth OncoOmics approach consisted in identifying genes that are essential for cancer cell proliferation and survival performing systematic loss-of-function screens in a large number of well-annotated cancer cell lines and BC cell lines representing the tumor heterogeneity 1821 . Figure 7A shows the distribution of dependency scores of 227/230 genes through DEMETER2, an analytical framework for analyzing genome-scale RNAi loss-of-function screens in 73 BC cell lines (Table S23).…”
Section: Resultsmentioning
confidence: 99%
“…The development of large-scale DNA sequencing, gene expression, proteomics, large-scale RNA interference (RNAi) screens and large-scale CRISPR-Cas9 screens has allowed us to better understand the molecular landscape of oncogenesis. Significant progress has been made in discovering gene coding regions 5 , cancer driver genes 6,7 , cancer driver mutations 8,9 , germline variants 10 , driver fusion genes 11,12 , alternatively spliced transcripts 13 , expression-based stratification 14 , molecular subtyping 15 , biomarkers 16 , druggable enzymes 17 , cancer dependencies 1821 , and drug sensitivity and resistance 22 .…”
Section: Introductionmentioning
confidence: 99%