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.
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2020-11-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-020-19015-1 |
| _version_ | 1818423138762883072 |
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| 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 |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-12-14T13:37:23Z |
| format | Article |
| id | doaj.art-bce8d3da8b7e49c7b03795b732b6ebd6 |
| institution | Directory Open Access Journal |
| issn | 2041-1723 |
| language | English |
| last_indexed | 2024-12-14T13:37:23Z |
| publishDate | 2020-11-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj.art-bce8d3da8b7e49c7b03795b732b6ebd62022-12-21T22:59:33ZengNature PortfolioNature 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 |
| 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 |
| title | 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_short | Benchmarking of cell type deconvolution pipelines for transcriptomics data |
| title_sort | benchmarking of cell type deconvolution pipelines for transcriptomics data |
| url | https://doi.org/10.1038/s41467-020-19015-1 |
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