Community assessment of methods to deconvolve cellular composition from bulk gene expression

Autores: White, Brian S.; de Reyniès, Aurélien; Newman, Aaron M.; Waterfall, Joshua J.; Lamb, Andrew; Petitprez, Florent; Lin, Yating; Yu, Rongshan; Guerrero Gimenez, Martin Eduardo; Domanskyi, Sergii; Monaco, Gianni; Chung, Verena; Banerjee, Jineta; Derrick, Daniel; Valdeolivas, Alberto; Li, Haojun; Xiao, Xu; Wang, Shun; Zheng, Frank; Yang, Wenxian; Catania, Carlos Adrian; Lang, Benjamin J.; Bertus, Thomas J.; Piermarocchi, Carlo; Caruso, Francesca P.; Scholz, Alexander; Saez Rodriguez, Julio; Heiser, Laura M.; Guinney, Justin; Gentles, Andrew J.
Año de publicación: 2024
Idioma: inglés
Tipo de recurso: artículo
Estado: versión publicada
Descripción: We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.
Fil: White, Brian S.. The Jackson Laboratory for Genomic Medicine; Estados Unidos
Fil: de Reyniès, Aurélien. Inserm; Francia. Universite de Paris; Francia
Fil: Newman, Aaron M.. University of Stanford; Estados Unidos
Fil: Waterfall, Joshua J.. Inserm; Francia. PSL Research University; Francia
Fil: Lamb, Andrew. Sage Bionetworks; Estados Unidos
Fil: Petitprez, Florent. University of Edinburgh; Reino Unido. Ligue Nationale Contre le Cancer; Francia
Fil: Lin, Yating. Xiamen University; China
Fil: Yu, Rongshan. Xiamen University; China
Fil: Guerrero Gimenez, Martin Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Domanskyi, Sergii. Michigan State University; Estados Unidos
Fil: Monaco, Gianni. BIOGEM Institute of Molecular Biology and Genetics; Italia
Fil: Chung, Verena. Sage Bionetworks; Estados Unidos
Fil: Banerjee, Jineta. Sage Bionetworks; Estados Unidos
Fil: Derrick, Daniel. Oregon Health & Science University; Estados Unidos
Fil: Valdeolivas, Alberto. Ruprecht Karls Universitat Heidelberg; Alemania
Fil: Li, Haojun. Xiamen University; China
Fil: Xiao, Xu. Xiamen University; China
Fil: Wang, Shun. Chinese Academy of Sciences; República de China
Fil: Zheng, Frank. AmoyDx; China
Fil: Yang, Wenxian. Aginome Scientific; China
Fil: Catania, Carlos Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina
Fil: Lang, Benjamin J.. Harvard Medical School; Estados Unidos
Fil: Bertus, Thomas J.. Michigan State University; Estados Unidos
Fil: Piermarocchi, Carlo. Michigan State University; Estados Unidos
Fil: Caruso, Francesca P.. BIOGEM Institute of Molecular Biology and Genetic; Italia
Fil: Scholz, Alexander. No especifíca;
Fil: Saez Rodriguez, Julio. Heidelberg University; Alemania
Fil: Heiser, Laura M.. Oregon Health & Science University; Estados Unidos
Fil: Guinney, Justin. Sage Bionetworks; Estados Unidos
Fil: Gentles, Andrew J.. University of Stanford; Estados Unidos
Materia: Support Vector Regression
Random Forests
Tumor Deconvolution
Nivel de accesibilidad: acceso abierto
Condiciones de uso: https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
Institución: Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador: oai:ri.conicet.gov.ar:11336/261172

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repository_id_str	3498
network_name_str	CONICET Digital (CONICET)
spelling	Community assessment of methods to deconvolve cellular composition from bulk gene expressionWhite, Brian S.de Reyniès, AurélienNewman, Aaron M.Waterfall, Joshua J.Lamb, AndrewPetitprez, FlorentLin, YatingYu, RongshanGuerrero Gimenez, Martin EduardoDomanskyi, SergiiMonaco, GianniChung, VerenaBanerjee, JinetaDerrick, DanielValdeolivas, AlbertoLi, HaojunXiao, XuWang, ShunZheng, FrankYang, WenxianCatania, Carlos AdrianLang, Benjamin J.Bertus, Thomas J.Piermarocchi, CarloCaruso, Francesca P.Scholz, AlexanderSaez Rodriguez, JulioHeiser, Laura M.Guinney, JustinGentles, Andrew J.Support Vector RegressionRandom ForestsTumor Deconvolutionhttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1https://purl.org/becyt/ford/1.6https://purl.org/becyt/ford/1We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.Fil: White, Brian S.. The Jackson Laboratory for Genomic Medicine; Estados UnidosFil: de Reyniès, Aurélien. Inserm; Francia. Universite de Paris; FranciaFil: Newman, Aaron M.. University of Stanford; Estados UnidosFil: Waterfall, Joshua J.. Inserm; Francia. PSL Research University; FranciaFil: Lamb, Andrew. Sage Bionetworks; Estados UnidosFil: Petitprez, Florent. University of Edinburgh; Reino Unido. Ligue Nationale Contre le Cancer; FranciaFil: Lin, Yating. Xiamen University; ChinaFil: Yu, Rongshan. Xiamen University; ChinaFil: Guerrero Gimenez, Martin Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Domanskyi, Sergii. Michigan State University; Estados UnidosFil: Monaco, Gianni. BIOGEM Institute of Molecular Biology and Genetics; ItaliaFil: Chung, Verena. Sage Bionetworks; Estados UnidosFil: Banerjee, Jineta. Sage Bionetworks; Estados UnidosFil: Derrick, Daniel. Oregon Health & Science University; Estados UnidosFil: Valdeolivas, Alberto. Ruprecht Karls Universitat Heidelberg; AlemaniaFil: Li, Haojun. Xiamen University; ChinaFil: Xiao, Xu. Xiamen University; ChinaFil: Wang, Shun. Chinese Academy of Sciences; República de ChinaFil: Zheng, Frank. AmoyDx; ChinaFil: Yang, Wenxian. Aginome Scientific; ChinaFil: Catania, Carlos Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; ArgentinaFil: Lang, Benjamin J.. Harvard Medical School; Estados UnidosFil: Bertus, Thomas J.. Michigan State University; Estados UnidosFil: Piermarocchi, Carlo. Michigan State University; Estados UnidosFil: Caruso, Francesca P.. BIOGEM Institute of Molecular Biology and Genetic; ItaliaFil: Scholz, Alexander. No especifíca;Fil: Saez Rodriguez, Julio. Heidelberg University; AlemaniaFil: Heiser, Laura M.. Oregon Health & Science University; Estados UnidosFil: Guinney, Justin. Sage Bionetworks; Estados UnidosFil: Gentles, Andrew J.. University of Stanford; Estados UnidosSpringer Nature2024-08info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/261172White, Brian S.; de Reyniès, Aurélien; Newman, Aaron M.; Waterfall, Joshua J.; Lamb, Andrew; et al.; Community assessment of methods to deconvolve cellular composition from bulk gene expression; Springer Nature; Nature Communications; 15; 1; 8-2024; 1-222041-1723CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-024-50618-0info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-024-50618-0info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:45:11Zoai:ri.conicet.gov.ar:11336/261172instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:45:12.097CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv	Community assessment of methods to deconvolve cellular composition from bulk gene expression
title	Community assessment of methods to deconvolve cellular composition from bulk gene expression
spellingShingle	Community assessment of methods to deconvolve cellular composition from bulk gene expression White, Brian S. Support Vector Regression Random Forests Tumor Deconvolution
title_short	Community assessment of methods to deconvolve cellular composition from bulk gene expression
title_full	Community assessment of methods to deconvolve cellular composition from bulk gene expression
title_fullStr	Community assessment of methods to deconvolve cellular composition from bulk gene expression
title_full_unstemmed	Community assessment of methods to deconvolve cellular composition from bulk gene expression
title_sort	Community assessment of methods to deconvolve cellular composition from bulk gene expression
dc.creator.none.fl_str_mv	White, Brian S. de Reyniès, Aurélien Newman, Aaron M. Waterfall, Joshua J. Lamb, Andrew Petitprez, Florent Lin, Yating Yu, Rongshan Guerrero Gimenez, Martin Eduardo Domanskyi, Sergii Monaco, Gianni Chung, Verena Banerjee, Jineta Derrick, Daniel Valdeolivas, Alberto Li, Haojun Xiao, Xu Wang, Shun Zheng, Frank Yang, Wenxian Catania, Carlos Adrian Lang, Benjamin J. Bertus, Thomas J. Piermarocchi, Carlo Caruso, Francesca P. Scholz, Alexander Saez Rodriguez, Julio Heiser, Laura M. Guinney, Justin Gentles, Andrew J.
author	White, Brian S.
author_facet	White, Brian S. de Reyniès, Aurélien Newman, Aaron M. Waterfall, Joshua J. Lamb, Andrew Petitprez, Florent Lin, Yating Yu, Rongshan Guerrero Gimenez, Martin Eduardo Domanskyi, Sergii Monaco, Gianni Chung, Verena Banerjee, Jineta Derrick, Daniel Valdeolivas, Alberto Li, Haojun Xiao, Xu Wang, Shun Zheng, Frank Yang, Wenxian Catania, Carlos Adrian Lang, Benjamin J. Bertus, Thomas J. Piermarocchi, Carlo Caruso, Francesca P. Scholz, Alexander Saez Rodriguez, Julio Heiser, Laura M. Guinney, Justin Gentles, Andrew J.
author_role	author
author2	de Reyniès, Aurélien Newman, Aaron M. Waterfall, Joshua J. Lamb, Andrew Petitprez, Florent Lin, Yating Yu, Rongshan Guerrero Gimenez, Martin Eduardo Domanskyi, Sergii Monaco, Gianni Chung, Verena Banerjee, Jineta Derrick, Daniel Valdeolivas, Alberto Li, Haojun Xiao, Xu Wang, Shun Zheng, Frank Yang, Wenxian Catania, Carlos Adrian Lang, Benjamin J. Bertus, Thomas J. Piermarocchi, Carlo Caruso, Francesca P. Scholz, Alexander Saez Rodriguez, Julio Heiser, Laura M. Guinney, Justin Gentles, Andrew J.
author2_role	author author author author author author author author author author author author author author author author author author author author author author author author author author author author author
dc.subject.none.fl_str_mv	Support Vector Regression Random Forests Tumor Deconvolution
topic	Support Vector Regression Random Forests Tumor Deconvolution
purl_subject.fl_str_mv	https://purl.org/becyt/ford/1.2 https://purl.org/becyt/ford/1 https://purl.org/becyt/ford/1.6 https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv	We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states. Fil: White, Brian S.. The Jackson Laboratory for Genomic Medicine; Estados Unidos Fil: de Reyniès, Aurélien. Inserm; Francia. Universite de Paris; Francia Fil: Newman, Aaron M.. University of Stanford; Estados Unidos Fil: Waterfall, Joshua J.. Inserm; Francia. PSL Research University; Francia Fil: Lamb, Andrew. Sage Bionetworks; Estados Unidos Fil: Petitprez, Florent. University of Edinburgh; Reino Unido. Ligue Nationale Contre le Cancer; Francia Fil: Lin, Yating. Xiamen University; China Fil: Yu, Rongshan. Xiamen University; China Fil: Guerrero Gimenez, Martin Eduardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina Fil: Domanskyi, Sergii. Michigan State University; Estados Unidos Fil: Monaco, Gianni. BIOGEM Institute of Molecular Biology and Genetics; Italia Fil: Chung, Verena. Sage Bionetworks; Estados Unidos Fil: Banerjee, Jineta. Sage Bionetworks; Estados Unidos Fil: Derrick, Daniel. Oregon Health & Science University; Estados Unidos Fil: Valdeolivas, Alberto. Ruprecht Karls Universitat Heidelberg; Alemania Fil: Li, Haojun. Xiamen University; China Fil: Xiao, Xu. Xiamen University; China Fil: Wang, Shun. Chinese Academy of Sciences; República de China Fil: Zheng, Frank. AmoyDx; China Fil: Yang, Wenxian. Aginome Scientific; China Fil: Catania, Carlos Adrian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mendoza. Instituto de Medicina y Biología Experimental de Cuyo; Argentina Fil: Lang, Benjamin J.. Harvard Medical School; Estados Unidos Fil: Bertus, Thomas J.. Michigan State University; Estados Unidos Fil: Piermarocchi, Carlo. Michigan State University; Estados Unidos Fil: Caruso, Francesca P.. BIOGEM Institute of Molecular Biology and Genetic; Italia Fil: Scholz, Alexander. No especifíca; Fil: Saez Rodriguez, Julio. Heidelberg University; Alemania Fil: Heiser, Laura M.. Oregon Health & Science University; Estados Unidos Fil: Guinney, Justin. Sage Bionetworks; Estados Unidos Fil: Gentles, Andrew J.. University of Stanford; Estados Unidos
description	We evaluate deconvolution methods, which infer levels of immune infiltration from bulk expression of tumor samples, through a community-wide DREAM Challenge. We assess six published and 22 community-contributed methods using in vitro and in silico transcriptional profiles of admixed cancer and healthy immune cells. Several published methods predict most cell types well, though they either were not trained to evaluate all functional CD8+ T cell states or do so with low accuracy. Several community-contributed methods address this gap, including a deep learning-based approach, whose strong performance establishes the applicability of this paradigm to deconvolution. Despite being developed largely using immune cells from healthy tissues, deconvolution methods predict levels of tumor-derived immune cells well. Our admixed and purified transcriptional profiles will be a valuable resource for developing deconvolution methods, including in response to common challenges we observe across methods, such as sensitive identification of functional CD4+ T cell states.
publishDate	2024
dc.date.none.fl_str_mv	2024-08
dc.type.none.fl_str_mv	info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo
format	article
status_str	publishedVersion
dc.identifier.none.fl_str_mv	http://hdl.handle.net/11336/261172 White, Brian S.; de Reyniès, Aurélien; Newman, Aaron M.; Waterfall, Joshua J.; Lamb, Andrew; et al.; Community assessment of methods to deconvolve cellular composition from bulk gene expression; Springer Nature; Nature Communications; 15; 1; 8-2024; 1-22 2041-1723 CONICET Digital CONICET
url	http://hdl.handle.net/11336/261172
identifier_str_mv	White, Brian S.; de Reyniès, Aurélien; Newman, Aaron M.; Waterfall, Joshua J.; Lamb, Andrew; et al.; Community assessment of methods to deconvolve cellular composition from bulk gene expression; Springer Nature; Nature Communications; 15; 1; 8-2024; 1-22 2041-1723 CONICET Digital CONICET
dc.language.none.fl_str_mv	eng
language	eng
dc.relation.none.fl_str_mv	info:eu-repo/semantics/altIdentifier/url/https://www.nature.com/articles/s41467-024-50618-0 info:eu-repo/semantics/altIdentifier/doi/10.1038/s41467-024-50618-0
dc.rights.none.fl_str_mv	info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv	openAccess
rights_invalid_str_mv	https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
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dc.publisher.none.fl_str_mv	Springer Nature
publisher.none.fl_str_mv	Springer Nature
dc.source.none.fl_str_mv	reponame:CONICET Digital (CONICET) instname:Consejo Nacional de Investigaciones Científicas y Técnicas
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repository.mail.fl_str_mv	dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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Community assessment of methods to deconvolve cellular composition from bulk gene expression

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