Skip to main content
bioRxiv
  • Home
  • About
  • Submit
  • ALERTS / RSS
Advanced Search
New Results

Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data

Khalil Ouardini, View ORCID ProfileRomain Lopez, View ORCID ProfileMatthew G. Jones, View ORCID ProfileSebastian Prillo, View ORCID ProfileRichard Zhang, View ORCID ProfileMichael I. Jordan, View ORCID ProfileNir Yosef
doi: https://doi.org/10.1101/2021.05.28.446021
Khalil Ouardini
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
2École CentraleSupélec, Gif-sur-Yvette, France
3École Normale Supérieure Paris-Saclay, Gif-sur-Yvette, France
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
Romain Lopez
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Romain Lopez
  • For correspondence: romain_lopez@berkeley.edu mattjones315@berkeley.edu niryosef@berkeley.edu
Matthew G. Jones
4Department of Cellular and Molecular Pharmacology, University of California, San Francisco, USA
5Biological and Medical Informatics Graduate Program, University of California, Berkeley, USA
7Center for Computational Biology, University of California, Berkeley, Berkeley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Matthew G. Jones
  • For correspondence: romain_lopez@berkeley.edu mattjones315@berkeley.edu niryosef@berkeley.edu
Sebastian Prillo
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Sebastian Prillo
Richard Zhang
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Richard Zhang
Michael I. Jordan
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
6Department of Statistics, University of California, Berkeley, USA
7Center for Computational Biology, University of California, Berkeley, Berkeley, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Michael I. Jordan
Nir Yosef
1Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, USA
7Center for Computational Biology, University of California, Berkeley, Berkeley, USA
8Ragon Institute of MGH, MIT and Harvard, USA
9Chan Zuckerberg Biohub, San Francisco, USA
  • Find this author on Google Scholar
  • Find this author on PubMed
  • Search for this author on this site
  • ORCID record for Nir Yosef
  • For correspondence: romain_lopez@berkeley.edu mattjones315@berkeley.edu niryosef@berkeley.edu
  • Abstract
  • Full Text
  • Info/History
  • Metrics
  • Data/Code
  • Preview PDF
Loading

Abstract

Novel experimental assays now simultaneously measure lineage relationships and transcriptomic states from single cells, thanks to CRISPR/Cas9-based genome engineering. These multimodal measurements allow researchers not only to build comprehensive phylogenetic models relating all cells but also infer transcriptomic determinants of consequential subclonal behavior. The gene expression data, however, is limited to cells that are currently present (“leaves” of the phylogeny). As a consequence, researchers cannot form hypotheses about unobserved, or “ancestral”, states that gave rise to the observed population. To address this, we introduce TreeVAE: a probabilistic framework for estimating ancestral transcriptional states. TreeVAE uses a variational autoencoder (VAE) to model the observed transcriptomic data while accounting for the phylogenetic relationships between cells. Using simulations, we demonstrate that TreeVAE outperforms benchmarks in reconstructing ancestral states on several metrics. TreeVAE also provides a measure of uncertainty, which we demonstrate to correlate well with its prediction accuracy. This estimate therefore potentially provides a data-driven way to estimate how far back in the ancestor chain predictions could be made. Finally, using real data from lung cancer metastasis, we show that accounting for phylogenetic relationship between cells improves goodness of fit. Together, TreeVAE provides a principled framework for reconstructing unobserved cellular states from single cell lineage tracing data.

Competing Interest Statement

NY is an advisor and/or has equity in Cellarity, Celsius Therapeutics, and Rheos Medicine.

Footnotes

  • https://github.com/khalilouardini/treeVAE-reproducibility

Copyright 
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.
Back to top
PreviousNext
Posted May 30, 2021.
Download PDF
Data/Code
Email

Thank you for your interest in spreading the word about bioRxiv.

NOTE: Your email address is requested solely to identify you as the sender of this article.

Enter multiple addresses on separate lines or separate them with commas.
Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
(Your Name) has forwarded a page to you from bioRxiv
(Your Name) thought you would like to see this page from the bioRxiv website.
CAPTCHA
This question is for testing whether or not you are a human visitor and to prevent automated spam submissions.
Share
Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
Khalil Ouardini, Romain Lopez, Matthew G. Jones, Sebastian Prillo, Richard Zhang, Michael I. Jordan, Nir Yosef
bioRxiv 2021.05.28.446021; doi: https://doi.org/10.1101/2021.05.28.446021
Reddit logo Twitter logo Facebook logo LinkedIn logo Mendeley logo
Citation Tools
Reconstructing unobserved cellular states from paired single-cell lineage tracing and transcriptomics data
Khalil Ouardini, Romain Lopez, Matthew G. Jones, Sebastian Prillo, Richard Zhang, Michael I. Jordan, Nir Yosef
bioRxiv 2021.05.28.446021; doi: https://doi.org/10.1101/2021.05.28.446021

Citation Manager Formats

  • BibTeX
  • Bookends
  • EasyBib
  • EndNote (tagged)
  • EndNote 8 (xml)
  • Medlars
  • Mendeley
  • Papers
  • RefWorks Tagged
  • Ref Manager
  • RIS
  • Zotero
  • Tweet Widget
  • Facebook Like
  • Google Plus One

Subject Area

  • Bioinformatics
Subject Areas
All Articles
  • Animal Behavior and Cognition (4667)
  • Biochemistry (10332)
  • Bioengineering (7653)
  • Bioinformatics (26277)
  • Biophysics (13497)
  • Cancer Biology (10663)
  • Cell Biology (15388)
  • Clinical Trials (138)
  • Developmental Biology (8480)
  • Ecology (12800)
  • Epidemiology (2067)
  • Evolutionary Biology (16817)
  • Genetics (11378)
  • Genomics (15451)
  • Immunology (10591)
  • Microbiology (25140)
  • Molecular Biology (10186)
  • Neuroscience (54317)
  • Paleontology (399)
  • Pathology (1663)
  • Pharmacology and Toxicology (2889)
  • Physiology (4331)
  • Plant Biology (9223)
  • Scientific Communication and Education (1585)
  • Synthetic Biology (2551)
  • Systems Biology (6769)
  • Zoology (1459)