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Title:
Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration | Nature Communications
Description:
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. Integrated analyses of multiple large-scale screenings can be complicated by batch effects and technical artefacts. McFarland et al. introduce DEMETER2, a hierarchical model coupled with model-based normalization, which allows the assessment of differential dependencies across genes and cell lines.
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gene, cell, dependency, genes, data, lines, estimates, fig, model, rnai, effects, datasets, achilles, dataset, shrna, line, differences, methods, dependencies, average, article, parameters, drive, correlation, essential, cancer, compared, number, screen, relationships, supplementary, screens, demeter, combined, expression, shrnas, quality, scores, models, google, scholar, genetic, relative, sigma, analysis, nature, correlations, copy, estimated, common,
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nature portfolio org/cancer/software/genepattern/modules/docs/ataris/1 privacy policy provided computational tools complex network research gov/refseq/h_sapiens/mrna_prot/human receives research funding novartis research foundation advertising author information authors io/bfg/ nature social media open source version crispr/cas9-based viability screens reprints references tsherniak crispr-cas9 essentiality screens genome-scale rnai screens genotype-specific cancer liabilities bio-primed machine learning original author identical shrna libraries parallel genome-scale loss protein–protein interaction data author correspondence crispr-cas9-based estimates rnai-inducing silencing complex stochastic variational expectation-maximization achilles crispr-cas9 dataset19 mathop{\mathrm{median}}\limits_{ mathop{\mathrm{median}}\limits_{ ccle portal assay-specific technical factors inter-cell line differences large-scale rnai screens context-specific genetic dependencies screen-quality related biases weak dose-dependency correlation pooled data—produced results large multi-screen datasets shrna-level depletion scores + \epsilon _{ijk}$$ top feature-dependency correlations full size image post-hoc correction methods positive/negative control separation �55k” libraries crispr-based estimates compared top dependency-feature relationships
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description: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 (
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description: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 (
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