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Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing

View ORCID ProfileDaniel B. Burkhardt, Jay S. Stanley III, Alexander Tong, View ORCID ProfileAna Luisa Perdigoto, View ORCID ProfileScott A. Gigante, View ORCID ProfileKevan C. Herold, Guy Wolf, View ORCID ProfileAntonio J. Giraldez, View ORCID ProfileDavid van Dijk, View ORCID ProfileSmita Krishnaswamy
doi: https://doi.org/10.1101/532846
Daniel B. Burkhardt
1Department of Genetics; Yale University, New Haven, CT, USA
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Jay S. Stanley III
3Computational Biology & Bioinformatics Program; Yale University, New Haven, CT, USA
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Alexander Tong
2Department of Computer Science; Yale University, New Haven, CT, USA
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Ana Luisa Perdigoto
4Department of Immunobiology; Yale University, New Haven, CT, USA
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  • ORCID record for Ana Luisa Perdigoto
Scott A. Gigante
1Department of Genetics; Yale University, New Haven, CT, USA
2Department of Computer Science; Yale University, New Haven, CT, USA
3Computational Biology & Bioinformatics Program; Yale University, New Haven, CT, USA
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Kevan C. Herold
4Department of Immunobiology; Yale University, New Haven, CT, USA
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Guy Wolf
6Department of Mathematics and Statistics, Université de Montréal, Montreal, QC, Canada
7Mila – Quebec AI Institute, Montreal, QC, Canada
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Antonio J. Giraldez
1Department of Genetics; Yale University, New Haven, CT, USA
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David van Dijk
1Department of Genetics; Yale University, New Haven, CT, USA
2Department of Computer Science; Yale University, New Haven, CT, USA
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Smita Krishnaswamy
1Department of Genetics; Yale University, New Haven, CT, USA
2Department of Computer Science; Yale University, New Haven, CT, USA
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  • For correspondence: smita.krishnaswamy@yale.edu
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Abstract

Single-cell RNA-sequencing (scRNA-seq) is a powerful tool to quantify transcriptional states in thousands to millions of cells. It is increasingly common for scRNA-seq data to be collected in multiple conditions to measure the effect of an experimental perturbation. However, quantifying differences between scRNA-seq datasets remains an analytical challenge. Previous efforts at quantifying such differences focus on discrete regions of the transcriptional state space such as clusters of cells. Here, we describe a continuous measure of the effect of an experiment across the transcriptomic space with single cell resolution. First, we use the manifold assumption to model the cellular state space as a graph with cells as nodes and edges connecting cells with similar transcriptomic profiles. Next, we calculate an Enhanced Experimental Signal (EES) that estimates the likelihood of observing cells from each condition at every point in the manifold. We show that the EES has useful properties for analysis of single cell perturbation studies. We show that we can use the magnitude and frequency of the EES, using an algorithm we call vertex frequency clustering, to identify specific populations of cells that are or are not affected by an experimental treatment at the appropriate level of granularity. Using these selected populations we can derive gene signatures of affected populations of cells. We demonstrate both algorithms using a combination of biological and synthetic datasets. Implementations are provided in the MELD Python package, which is available at https://github.com/KrishnaswamyLab/MELD.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • Every section has had major revisions, the EES algorithm has been significantly updated, the analysis of previously published datasets has been expanded, and the benchmarking section has been improved. We also clarified the text introducing the EES and VFC algorithms. We hope that this will clear up some of the questions from the reviewers regarding the application of these algorithms.

  • https://github.com/krishnaswamylab/MELD

  • 2 Abbreviations:

    MLP
    Lateral plate
    TPM
    Tailbud - Presomitic mesoderm
    HG
    Hatching gland
    MBI
    Blood island
    EPP
    Epidermal - pfn1
    MEN
    Endothelial
    PRD
    Periderm
    EPA
    Epidermal anterior
    EPO
    Otic placode
    LLP
    Lateral line
    EPF
    Epi-dermal - foxi3a
    GL
    Germline
    NRB
    Rohon beard
    NFP
    Floorplate
    MHF
    Heart field
    MPA
    Pharyngeal arch
    NCC
    Neural crest - crestin
    END
    Endoderm
    TSC
    Tailbud - spinal cord
    NC
    Neural crest
    NTE
    Telencephalon
    MPD
    Pronephric duct
    NHB
    Hindbrain
    NMB
    Midbrain
    NTC
    Notocord
    NDI
    Diencephalon
    DN
    Neurons
    OP
    Optic

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-NC-ND 4.0 International license.
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Posted August 01, 2020.
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Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing
Daniel B. Burkhardt, Jay S. Stanley III, Alexander Tong, Ana Luisa Perdigoto, Scott A. Gigante, Kevan C. Herold, Guy Wolf, Antonio J. Giraldez, David van Dijk, Smita Krishnaswamy
bioRxiv 532846; doi: https://doi.org/10.1101/532846
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Quantifying the effect of experimental perturbations in single-cell RNA-sequencing data using graph signal processing
Daniel B. Burkhardt, Jay S. Stanley III, Alexander Tong, Ana Luisa Perdigoto, Scott A. Gigante, Kevan C. Herold, Guy Wolf, Antonio J. Giraldez, David van Dijk, Smita Krishnaswamy
bioRxiv 532846; doi: https://doi.org/10.1101/532846

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