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Protein velocity and acceleration from single-cell multiomics experiments
The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages e...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029606/ https://www.ncbi.nlm.nih.gov/pubmed/32070398 http://dx.doi.org/10.1186/s13059-020-1945-3 |
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author | Gorin, Gennady Svensson, Valentine Pachter, Lior |
author_facet | Gorin, Gennady Svensson, Valentine Pachter, Lior |
author_sort | Gorin, Gennady |
collection | PubMed |
description | The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances to extrapolate cell states. We apply the model to six datasets and demonstrate consistency among cell landscapes and phase portraits. The analysis software is available as the protaccel Python package. |
format | Online Article Text |
id | pubmed-7029606 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-70296062020-02-25 Protein velocity and acceleration from single-cell multiomics experiments Gorin, Gennady Svensson, Valentine Pachter, Lior Genome Biol Short Report The simultaneous quantification of protein and RNA makes possible the inference of past, present, and future cell states from single experimental snapshots. To enable such temporal analysis from multimodal single-cell experiments, we introduce an extension of the RNA velocity method that leverages estimates of unprocessed transcript and protein abundances to extrapolate cell states. We apply the model to six datasets and demonstrate consistency among cell landscapes and phase portraits. The analysis software is available as the protaccel Python package. BioMed Central 2020-02-18 /pmc/articles/PMC7029606/ /pubmed/32070398 http://dx.doi.org/10.1186/s13059-020-1945-3 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Short Report Gorin, Gennady Svensson, Valentine Pachter, Lior Protein velocity and acceleration from single-cell multiomics experiments |
title | Protein velocity and acceleration from single-cell multiomics experiments |
title_full | Protein velocity and acceleration from single-cell multiomics experiments |
title_fullStr | Protein velocity and acceleration from single-cell multiomics experiments |
title_full_unstemmed | Protein velocity and acceleration from single-cell multiomics experiments |
title_short | Protein velocity and acceleration from single-cell multiomics experiments |
title_sort | protein velocity and acceleration from single-cell multiomics experiments |
topic | Short Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7029606/ https://www.ncbi.nlm.nih.gov/pubmed/32070398 http://dx.doi.org/10.1186/s13059-020-1945-3 |
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