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Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets
Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple C...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
eLife Sciences Publications, Ltd
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500954/ https://www.ncbi.nlm.nih.gov/pubmed/32894218 http://dx.doi.org/10.7554/eLife.59630 |
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author | Trussart, Marie Teh, Charis E Tan, Tania Leong, Lawrence Gray, Daniel HD Speed, Terence P |
author_facet | Trussart, Marie Teh, Charis E Tan, Tania Leong, Lawrence Gray, Daniel HD Speed, Terence P |
author_sort | Trussart, Marie |
collection | PubMed |
description | Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes. |
format | Online Article Text |
id | pubmed-7500954 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | eLife Sciences Publications, Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-75009542020-09-21 Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets Trussart, Marie Teh, Charis E Tan, Tania Leong, Lawrence Gray, Daniel HD Speed, Terence P eLife Computational and Systems Biology Mass cytometry (CyTOF) is a technology that has revolutionised single-cell biology. By detecting over 40 proteins on millions of single cells, CyTOF allows the characterisation of cell subpopulations in unprecedented detail. However, most CyTOF studies require the integration of data from multiple CyTOF batches usually acquired on different days and possibly at different sites. To date, the integration of CyTOF datasets remains a challenge due to technical differences arising in multiple batches. To overcome this limitation, we developed an approach called CytofRUV for analysing multiple CyTOF batches, which includes an R-Shiny application with diagnostic plots. CytofRUV can correct for batch effects and integrate data from large numbers of patients and conditions across batches, to confidently compare cellular changes and correlate these with clinically relevant outcomes. eLife Sciences Publications, Ltd 2020-09-07 /pmc/articles/PMC7500954/ /pubmed/32894218 http://dx.doi.org/10.7554/eLife.59630 Text en © 2020, Trussart et al http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/This article is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use and redistribution provided that the original author and source are credited. |
spellingShingle | Computational and Systems Biology Trussart, Marie Teh, Charis E Tan, Tania Leong, Lawrence Gray, Daniel HD Speed, Terence P Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title | Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title_full | Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title_fullStr | Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title_full_unstemmed | Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title_short | Removing unwanted variation with CytofRUV to integrate multiple CyTOF datasets |
title_sort | removing unwanted variation with cytofruv to integrate multiple cytof datasets |
topic | Computational and Systems Biology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7500954/ https://www.ncbi.nlm.nih.gov/pubmed/32894218 http://dx.doi.org/10.7554/eLife.59630 |
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