Benchmarking of cell type deconvolution pipelines for transcriptomics data

Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.

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Auteurs principaux:	Francisco Avila Cobos, José Alquicira-Hernandez, Joseph E. Powell, Pieter Mestdagh, Katleen De Preter
Format:	article
Langue:	EN
Publié:	Nature Portfolio 2020
Sujets:	Science Q
Accès en ligne:	https://doaj.org/article/bce8d3da8b7e49c7b03795b732b6ebd6
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id	oai:doaj.org-article:bce8d3da8b7e49c7b03795b732b6ebd6
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spelling	oai:doaj.org-article:bce8d3da8b7e49c7b03795b732b6ebd62021-12-02T15:39:12ZBenchmarking of cell type deconvolution pipelines for transcriptomics data10.1038/s41467-020-19015-12041-1723https://doaj.org/article/bce8d3da8b7e49c7b03795b732b6ebd62020-11-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-19015-1https://doaj.org/toc/2041-1723Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.Francisco Avila CobosJosé Alquicira-HernandezJoseph E. PowellPieter MestdaghKatleen De PreterNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-14 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Francisco Avila Cobos José Alquicira-Hernandez Joseph E. Powell Pieter Mestdagh Katleen De Preter Benchmarking of cell type deconvolution pipelines for transcriptomics data
description	Inferring cell type proportions from transcriptomics data is affected by data transformation, normalization, choice of method and the markers used. Here, the authors use single-cell RNAseq datasets to evaluate the impact of these factors and propose guidelines to maximise deconvolution performance.
format	article
author	Francisco Avila Cobos José Alquicira-Hernandez Joseph E. Powell Pieter Mestdagh Katleen De Preter
author_facet	Francisco Avila Cobos José Alquicira-Hernandez Joseph E. Powell Pieter Mestdagh Katleen De Preter
author_sort	Francisco Avila Cobos
title	Benchmarking of cell type deconvolution pipelines for transcriptomics data
title_short	Benchmarking of cell type deconvolution pipelines for transcriptomics data
title_full	Benchmarking of cell type deconvolution pipelines for transcriptomics data
title_fullStr	Benchmarking of cell type deconvolution pipelines for transcriptomics data
title_full_unstemmed	Benchmarking of cell type deconvolution pipelines for transcriptomics data
title_sort	benchmarking of cell type deconvolution pipelines for transcriptomics data
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/bce8d3da8b7e49c7b03795b732b6ebd6
work_keys_str_mv	AT franciscoavilacobos benchmarkingofcelltypedeconvolutionpipelinesfortranscriptomicsdata AT josealquicirahernandez benchmarkingofcelltypedeconvolutionpipelinesfortranscriptomicsdata AT josephepowell benchmarkingofcelltypedeconvolutionpipelinesfortranscriptomicsdata AT pietermestdagh benchmarkingofcelltypedeconvolutionpipelinesfortranscriptomicsdata AT katleendepreter benchmarkingofcelltypedeconvolutionpipelinesfortranscriptomicsdata
_version_	1718386012923625472

Benchmarking of cell type deconvolution pipelines for transcriptomics data

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