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scPerturb: Harmonized Single-Cell Perturbation Data

View ORCID ProfileStefan Peidli, View ORCID ProfileTessa D. Green, View ORCID ProfileCiyue Shen, Torsten Gross, View ORCID ProfileJoseph Min, Samuele Garda, Bo Yuan, View ORCID ProfileLinus J. Schumacher, View ORCID ProfileJake P. Taylor-King, View ORCID ProfileDebora S. Marks, View ORCID ProfileAugustin Luna, View ORCID ProfileNils Blüthgen, View ORCID ProfileChris Sander
doi: https://doi.org/10.1101/2022.08.20.504663
Stefan Peidli
1Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
2IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
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  • ORCID record for Stefan Peidli
Tessa D. Green
3Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Ciyue Shen
4Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
5Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
6Broad Institute, Cambridge, MA, USA
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Torsten Gross
7Relation Therapeutics, London, UK
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Joseph Min
3Department of Systems Biology, Harvard Medical School, Boston, MA, USA
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Samuele Garda
2IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
8Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany
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Bo Yuan
4Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
5Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
6Broad Institute, Cambridge, MA, USA
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Linus J. Schumacher
9Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK
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  • ORCID record for Linus J. Schumacher
Jake P. Taylor-King
7Relation Therapeutics, London, UK
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Debora S. Marks
3Department of Systems Biology, Harvard Medical School, Boston, MA, USA
6Broad Institute, Cambridge, MA, USA
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  • ORCID record for Debora S. Marks
Augustin Luna
4Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
5Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
6Broad Institute, Cambridge, MA, USA
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Nils Blüthgen
1Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany
2IRI Life Sciences, Humboldt-Universität zu Berlin, Berlin, Germany
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Chris Sander
4Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA
5Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA
6Broad Institute, Cambridge, MA, USA
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  • For correspondence: chris@sanderlab.org
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Abstract

Recent biotechnological advances led to growing numbers of single-cell perturbation studies, which reveal molecular and phenotypic responses to large numbers of perturbations. However, analysis across diverse datasets is typically hampered by differences in format, naming conventions, and data filtering. In order to facilitate development and benchmarking of computational methods in systems biology, we collect a set of 44 publicly available single-cell perturbation-response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform pre-processing and quality control pipelines and harmonize feature annotations. The resulting information resource enables efficient development and testing of computational analysis methods, and facilitates direct comparison and integration across datasets. In addition, we introduce E-statistics for perturbation effect quantification and significance testing, and demonstrate E-distance as a general distance measure for single cell data. Using these datasets, we illustrate the application of E-statistics for quantifying perturbation similarity and efficacy. The data and a package for computing E-statistics is publicly available at scperturb.org. This work provides an information resource and guide for researchers working with single-cell perturbation data, highlights conceptual considerations for new experiments, and makes concrete recommendations for optimal cell counts and read depth.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • x joint first authors

  • ↵+ joint senior authors

  • Added additional figures and analysis of E-statistics

  • http://scperturb.org

  • https://github.com/sanderlab/scPerturb

  • https://zenodo.org/record/7058382

  • https://zenodo.org/record/7041849

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted January 25, 2023.
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scPerturb: Harmonized Single-Cell Perturbation Data
Stefan Peidli, Tessa D. Green, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Bo Yuan, Linus J. Schumacher, Jake P. Taylor-King, Debora S. Marks, Augustin Luna, Nils Blüthgen, Chris Sander
bioRxiv 2022.08.20.504663; doi: https://doi.org/10.1101/2022.08.20.504663
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scPerturb: Harmonized Single-Cell Perturbation Data
Stefan Peidli, Tessa D. Green, Ciyue Shen, Torsten Gross, Joseph Min, Samuele Garda, Bo Yuan, Linus J. Schumacher, Jake P. Taylor-King, Debora S. Marks, Augustin Luna, Nils Blüthgen, Chris Sander
bioRxiv 2022.08.20.504663; doi: https://doi.org/10.1101/2022.08.20.504663

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