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RNA velocity unraveled

Gorin, Gennady and Fang, Meichen and Chari, Tara and Pachter, Lior (2022) RNA velocity unraveled. . (Unpublished) https://resolver.caltech.edu/CaltechAUTHORS:20220214-147056100

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Abstract

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
https://doi.org/10.1101/2022.02.12.480214DOIDiscussion Paper
https://support.10xgenomics.com/single-cell-gene-expression/datasetsRelated ItemData
http://pklab.med.harvard.edu/velocyto/hgForebrainGlut/hgForebrainGlut.loomRelated ItemData
https://data.caltech.edu/records/20030Related ItemData
https://github.com/pachterlab/GFCP_2022Related ItemCode
ORCID:
AuthorORCID
Gorin, Gennady0000-0001-6097-2029
Fang, Meichen0000-0002-8217-0710
Chari, Tara0000-0002-6953-4313
Pachter, Lior0000-0002-9164-6231
Additional Information:The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license. This version posted February 13, 2022. G.G., M.F., T.C., and L.P. were partially funded by NIH U19MH114830. The DNA and RNA illustrations used in Figure 3 are derived from the DNA Twemoji by Twitter, Inc., used under CC-BY 4.0. The palette used in Figures 6, 8, 10, S3, and S5 is derived from dutchmasters by EdwinTh. G.G. thanks Dr. John J. Vastola for fruitful discussions about landscape representations of biophysical systems. Data Availability: The datasets analyzed for Section 3.1, as outlined in Section 6.1, are listed in Table 1. The datasets released by Desai et al. were collated from the Sequence Read Archive (runs SRR14713295 for dmso and SRR14713295 for idu) [155]. The datasets released by 10x Genomics were obtained from https://support.10xgenomics.com/single-cell-gene-expression/datasets. The processed human forebrain dataset generated by La Manno et al. [1] was obtained from http://pklab.med.harvard.edu/velocyto/hgForebrainGlut/hgForebrainGlut.loom, as used in the velocyto documentation. The processed loom files generated by the three work flows are available at the CaltechData repository, at https://data.caltech.edu/records/20030. All Python scripts and notebooks necessary to reproduce the results of this study are available at https://github.com/pachterlab/GFCP_2022. Author Contributions: Conceived of the project: G.G. and L.P. Wrote scripts/notebooks for pre-processing, simulation, and analysis: G.G., M.F., and T.C. Analyzed and interpreted the data: G.G., M.F., T.C., and L.P. Wrote and edited the manuscript: G.G., M.F., T.C. and L.P. The authors have declared no competing interest.
Funders:
Funding AgencyGrant Number
NIHU19MH114830
DOI:10.1101/2022.02.12.480214
Record Number:CaltechAUTHORS:20220214-147056100
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20220214-147056100
Official Citation:RNA velocity unraveled. Gennady Gorin, Meichen Fang, Tara Chari, Lior Pachter. bioRxiv 2022.02.12.480214; doi: https://doi.org/10.1101/2022.02.12.480214
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:113438
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:14 Feb 2022 19:13
Last Modified:14 Feb 2022 19:13

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