Published February 18, 2020
| Submitted + Published + Supplemental Material
Journal Article
Open
RNA velocity and protein acceleration from single-cell multiomics experiments
Chicago
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Abstract
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.
Additional Information
© 2020 The Author(s). Open Access This 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. Received 09 July 2019; Accepted 24 January 2020; Published 18 February 2020. We thank the authors of Mimitou et al. [5] for providing velocyto pipeline outputs for ECCITE-seq datasets. Review history: The review history is available as Additional file 2. Peer review information: Barbara Cheifet was the primary editor of this article and managed its editorial process and peer review in collaboration with the rest of the editorial team. GG, VS, and LP were partially funded by NIH U19MH114830. Availability of data and materials: CITE-seq RNA and protein data were acquired from Gene Expression Omnibus samples GSM2695381 and GSM2695382 [31]. REAP-seq RNA and protein data were acquired from GSM2685238 and GSM2685243 [32]. ECCITE-seq control protein data were acquired from GSM3596096 [33]. ECCITE-seq CTCL protein data were acquired from GSM3596101 [33]. Due to patient privacy concerns, raw ECCITE-seq RNA data (GSM3596095 and GSM3596100) were not available, and the gene count matrices generated by velocyto were acquired by personal request. 10X Genomics 1k and 10k PBMC datasets were acquired from the 10X Genomics website [20, 21]. The datasets generated during this study are available on figshare [27,28,29,30].The Jupyter scripts used to analyze them are available on GitHub [26]. The protaccel Python package is available for installation through PyPi [24], and may be acquired as a script from GitHub [26] or Zenodo [34] under the BSD-2-Clause license. Ethics declarations: Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. The authors declare that they have no competing interests.Attached Files
Published - s13059-020-1945-3.pdf
Submitted - 658401.full.pdf
Supplemental Material - 13059_2020_1945_MOESM1_ESM.docx
Supplemental Material - 13059_2020_1945_MOESM2_ESM.docx
Files
s13059-020-1945-3.pdf
Additional details
- PMCID
- PMC7029606
- Eprint ID
- 96201
- Resolver ID
- CaltechAUTHORS:20190607-122759859
- NIH
- U19MH114830
- Created
-
2019-06-07Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field
- Caltech groups
- Division of Biology and Biological Engineering (BBE)