Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.

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Autores principales:	Francesco Vallania, Andrew Tam, Shane Lofgren, Steven Schaffert, Tej D. Azad, Erika Bongen, Winston Haynes, Meia Alsup, Michael Alonso, Mark Davis, Edgar Engleman, Purvesh Khatri
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2018
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/6f12c79a854b4734a673942857dc8a8a
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spelling	oai:doaj.org-article:6f12c79a854b4734a673942857dc8a8a2021-12-02T14:40:05ZLeveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases10.1038/s41467-018-07242-62041-1723https://doaj.org/article/6f12c79a854b4734a673942857dc8a8a2018-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-018-07242-6https://doaj.org/toc/2041-1723Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.Francesco VallaniaAndrew TamShane LofgrenSteven SchaffertTej D. AzadErika BongenWinston HaynesMeia AlsupMichael AlonsoMark DavisEdgar EnglemanPurvesh KhatriNature PortfolioarticleScienceQENNature Communications, Vol 9, Iss 1, Pp 1-8 (2018)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Francesco Vallania Andrew Tam Shane Lofgren Steven Schaffert Tej D. Azad Erika Bongen Winston Haynes Meia Alsup Michael Alonso Mark Davis Edgar Engleman Purvesh Khatri Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
description	Cell type deconvolution from bulk expression data rely on a reference expression matrix. Here, the authors introduce a basis matrix built using data from both healthy and diseased samples profiled on 42 platforms, reducing biases introduced by single-platform matrices built using healthy samples.
format	article
author	Francesco Vallania Andrew Tam Shane Lofgren Steven Schaffert Tej D. Azad Erika Bongen Winston Haynes Meia Alsup Michael Alonso Mark Davis Edgar Engleman Purvesh Khatri
author_facet	Francesco Vallania Andrew Tam Shane Lofgren Steven Schaffert Tej D. Azad Erika Bongen Winston Haynes Meia Alsup Michael Alonso Mark Davis Edgar Engleman Purvesh Khatri
author_sort	Francesco Vallania
title	Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
title_short	Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
title_full	Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
title_fullStr	Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
title_full_unstemmed	Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
title_sort	leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases
publisher	Nature Portfolio
publishDate	2018
url	https://doaj.org/article/6f12c79a854b4734a673942857dc8a8a
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Leveraging heterogeneity across multiple datasets increases cell-mixture deconvolution accuracy and reduces biological and technical biases

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