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

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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Autores principales:	James M. McFarland, Zandra V. Ho, Guillaume Kugener, Joshua M. Dempster, Phillip G. Montgomery, Jordan G. Bryan, John M. Krill-Burger, Thomas M. Green, Francisca Vazquez, Jesse S. Boehm, Todd R. Golub, William C. Hahn, David E. Root, Aviad Tsherniak
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/d0eb361d0bb14b90ba18bfe3b6bb1fd5
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spelling	oai:doaj.org-article:d0eb361d0bb14b90ba18bfe3b6bb1fd52021-12-02T14:39:10ZImproved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration10.1038/s41467-018-06916-52041-1723https://doaj.org/article/d0eb361d0bb14b90ba18bfe3b6bb1fd52018-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-06916-5https://doaj.org/toc/2041-1723Integrated 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.James M. McFarlandZandra V. HoGuillaume KugenerJoshua M. DempsterPhillip G. MontgomeryJordan G. BryanJohn M. Krill-BurgerThomas M. GreenFrancisca VazquezJesse S. BoehmTodd R. GolubWilliam C. HahnDavid E. RootAviad TsherniakNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-13 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q James M. McFarland Zandra V. Ho Guillaume Kugener Joshua M. Dempster Phillip G. Montgomery Jordan G. Bryan John M. Krill-Burger Thomas M. Green Francisca Vazquez Jesse S. Boehm Todd R. Golub William C. Hahn David E. Root Aviad Tsherniak Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
description	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.
format	article
author	James M. McFarland Zandra V. Ho Guillaume Kugener Joshua M. Dempster Phillip G. Montgomery Jordan G. Bryan John M. Krill-Burger Thomas M. Green Francisca Vazquez Jesse S. Boehm Todd R. Golub William C. Hahn David E. Root Aviad Tsherniak
author_facet	James M. McFarland Zandra V. Ho Guillaume Kugener Joshua M. Dempster Phillip G. Montgomery Jordan G. Bryan John M. Krill-Burger Thomas M. Green Francisca Vazquez Jesse S. Boehm Todd R. Golub William C. Hahn David E. Root Aviad Tsherniak
author_sort	James M. McFarland
title	Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
title_short	Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
title_full	Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
title_fullStr	Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
title_full_unstemmed	Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration
title_sort	improved estimation of cancer dependencies from large-scale rnai screens using model-based normalization and data integration
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/d0eb361d0bb14b90ba18bfe3b6bb1fd5
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Improved estimation of cancer dependencies from large-scale RNAi screens using model-based normalization and data integration

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