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.

Bibliographic Details
Main Authors:	Francisco Avila Cobos, José Alquicira-Hernandez, Joseph E. Powell, Pieter Mestdagh, Katleen De Preter
Format:	Article
Language:	English
Published:	Nature Publishing Group 2020-11-01
Series:	Nature Communications
Online Access:	https://doi.org/10.1038/s41467-020-19015-1

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spelling	doaj-bce8d3da8b7e49c7b03795b732b6ebd62021-05-11T08:29:23ZengNature Publishing GroupNature Communications2041-17232020-11-0111111410.1038/s41467-020-19015-1Benchmarking of cell type deconvolution pipelines for transcriptomics dataFrancisco Avila Cobos0José Alquicira-Hernandez1Joseph E. Powell2Pieter Mestdagh3Katleen De Preter4Center for Medical Genetics Ghent, Department of Biomolecular Medicine, Ghent UniversityGarvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical ResearchGarvan Weizmann Centre for Cellular Genomics, Garvan Institute of Medical ResearchCenter for Medical Genetics Ghent, Department of Biomolecular Medicine, Ghent UniversityCenter for Medical Genetics Ghent, Department of Biomolecular Medicine, Ghent UniversityInferring 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.https://doi.org/10.1038/s41467-020-19015-1
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Francisco Avila Cobos José Alquicira-Hernandez Joseph E. Powell Pieter Mestdagh Katleen De Preter
spellingShingle	Francisco Avila Cobos José Alquicira-Hernandez Joseph E. Powell Pieter Mestdagh Katleen De Preter Benchmarking of cell type deconvolution pipelines for transcriptomics data Nature Communications
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 Publishing Group
series	Nature Communications
issn	2041-1723
publishDate	2020-11-01
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.
url	https://doi.org/10.1038/s41467-020-19015-1
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Benchmarking of cell type deconvolution pipelines for transcriptomics data

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