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scPerturb: harmonized single-cell perturbation data

Nature Methods volume 21, pages 531–540 (2024)Cite this article

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Abstract

Analysis across a growing number of single-cell perturbation datasets is hampered by poor data interoperability. To facilitate development and benchmarking of computational methods, we collect a set of 44 publicly available single-cell perturbation–response datasets with molecular readouts, including transcriptomics, proteomics and epigenomics. We apply uniform quality control pipelines and harmonize feature annotations. The resulting information resource, scPerturb, enables development and testing of computational methods, and facilitates comparison and integration across datasets. We describe energy statistics (E-statistics) for quantification of perturbation effects and significance testing, and demonstrate E-distance as a general distance measure between sets of single-cell expression profiles. We illustrate the application of E-statistics for quantifying similarity and efficacy of perturbations. The perturbation–response datasets and E-statistics computation software are publicly available at scperturb.org. This work provides an information resource for researchers working with single-cell perturbation data and recommendations for experimental design, including optimal cell counts and read depth.

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Fig. 1: Perturbation–response profiling for single cells.
Fig. 2: Single-cell perturbation–response datasets are diverse in type, size and quality.
Fig. 3: E-statistics describe distinctiveness of perturbations in single-cell data.
Fig. 4: E-distance dissects perturbation hierarchy in data from Papalexi et al.9.
Fig. 5: Effect of subsampling UMI counts per cell and number of cells per perturbation on E-statistics.

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Data availability

The website scperturb.org stores harmonized datasets with the following: scRNA-seq and antibody-based protein datasets: .h5ad files; scATAC-seq: multiple different feature matrix definitions as separate download options. RNA data at https://doi.org/10.5281/zenodo.7041848 and ATAC data at https://doi.org/10.5281/zenodo.7058381. Dataset access details: AdamsonWeissman20167: GSE90546 on GEO55; AissaBenevolenskaya202168: GSE149383 on GEO; ChangYe202169: E-MTAB-10698 on ArrayExpress70; DatlingerBock20171: GSE92872 on GEO; DatlingerBock202171: GSE168620 on GEO; DixitRegev20162: GSE90063 on GEO; FrangiehIzar20216: SCP1064 on the Broad Single Cell Portal https://singlecell.broadinstitute.org/single_cell/study/SCP1064/multi-modal-pooled-perturb-cite-seq-screens-in-patient-models-define-novel-mechanisms-of-cancer-immune-evasion; GasperiniShendure201954: GSE120861 on GEO; GehringPachter201919: https://doi.org/10.22002/D1.1311 on CaltechDATA; Liscovitch-BrauerSanjana202159: GSE161002 on GEO; McFarlandTsherniak202072: https://doi.org/10.6084/m9.figshare.5863776.v1 on figshare; MimitouSmibert202173: GSE156476 on GEO; NormanWeissman201949: GSE133344 on GEO; PapalexiSatija2029: GSE153056 on GEO; PierceGreenleaf202142: data deposited on AWS, URIs to be found at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8137922/bin/41467_2021_23213_MOESM9_ESM.xlsx; ReplogleWeissman202220: processed single-cell data from gwps.wi.mit.edu; SchiebingerLander201923: GSE106340 and GSE115943 on GEO; SchraivogelSteinmetz202074: GSE135497 on GEO; ShifrutMarson201875: GSE119450 on GEO; SrivatsanTrapnell202052: GSE139944 on GEO; TianKampmann201976: GSE152988 on GEO with mappings from kampmannlab.ucsf.edu/crop-seq; TianKampmann202121: GSE124703 on GEO; WeinrebKlein202077: GSE140802 on GEO; XieHon201778: GSE81884 on GEO; ZhaoSims202179: GSE148842 on GEO.

Code availability

Open access source code is at https://github.com/sanderlab/scPerturb/. We compiled a corresponding Python package called scperturb for performing E-statistics (E-distance and E-testing) in single-cell data, published on PyPI under https://pypi.org/project/scperturb/. Access details for the original publication for each dataset are available in the scPerturb GitHub repository (https://github.com/sanderlab/scPerturb) in the subfolder 'dataset_processing'.

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Acknowledgements

The authors appreciate informative conversations with Y. Ji of the Fabian Theis laboratory, helpful code suggestions from G. Wong, and computational support from A. Kollasch of the Debora Marks laboratory. The authors also appreciate preprint review comments from Arcadia Science’s preprint review initiative (G. P. Way, N. Davidson, E. Serrano, P. Hicks, J. Tomkinson, D. Bunten). This work was supported by the National Resource for Network Biology (NRNB, P41GM103504 to C.Sa.), the Wellcome Leap ∆Tissue Program (to C.Sa., L.J.S., D.S.M.), the Deutsche Forschungsgemeinschaft (DFG, RTG2424 CompCancer to N.B.), Einstein Stiftung Berlin (Einstein Visiting Fellow program, to C.Sa., N.B.), and the Intramural Research Program of the National Library of Medicine, National Institutes of Health (to A.L.). Computation was in part performed on the HPC for Research cluster of the Berlin Institute of Health. Figures 1 and 4b were created with BioRender.com.

Author information

Author notes

  1. These authors contributed equally: Stefan Peidli, Tessa D. Green.

  2. These authors jointly supervised this work: Nils Blüthgen, Chris Sander.

Authors and Affiliations

  1. Institute of Pathology, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt-Universität, Berlin, Germany

    Stefan Peidli & Nils Blüthgen

  2. Institute of Biology, Humboldt-Universität, Berlin, Germany

    Stefan Peidli, Samuele Garda & Nils Blüthgen

  3. Department of Systems Biology, Harvard Medical School, Boston, MA, USA

    Tessa D. Green, Joseph Min & Debora S. Marks

  4. Departments of Cell Biology and Systems Biology, Harvard Medical School, Boston, MA, USA

    Ciyue Shen, Bo Yuan, Augustin Luna & Chris Sander

  5. Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA

    Ciyue Shen, Bo Yuan, Augustin Luna & Chris Sander

  6. Broad Institute, Cambridge, MA, USA

    Ciyue Shen, Bo Yuan, Debora S. Marks, Augustin Luna & Chris Sander

  7. Relation Therapeutics, London, UK

    Torsten Gross & Jake P. Taylor-King

  8. Institute for Computer Science, Humboldt-Universität zu Berlin, Berlin, Germany

    Samuele Garda

  9. Centre for Regenerative Medicine, University of Edinburgh, Edinburgh, UK

    Linus J. Schumacher

  10. Computational Biology Branch, National Library of Medicine and Developmental Therapeutics Branch, National Cancer Institute, Bethesda, MD, USA

    Augustin Luna

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Contributions

The project was conceptualized by C.Sa., N.B., A.L. and B.Y. Data were curated by T.D.G., S.P., C.Sh., T.G. and S.G. Formal analysis and methodology development were carried out by S.P., T.D.G. and C.Sa. Funding acquisition was done by N.B., D.S.M., L.J.S. and C.Sa. Software development was carried out by J.M., S.P. and T.D.G. Supervision was provided by N.B., A.L., J.P.T.-K., C.Sa., D.S.M. and L.J.S. The original draft was written by T.D.G., S.P., C.Sh., T.G. and J.P.T.-K. Writing review and editing were done by L.J.S., C.Sa., N.B. and A.L.

Corresponding authors

Correspondence to Stefan Peidli, Augustin Luna, Nils Blüthgen or Chris Sander.

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Competing interests

J.P.T.-K. and T.G. are employees of Relation Therapeutics. C.Sa. is on the science advisory board of Cytoreason Ltd. D.S.M. serves as an advisor for Dyno Therapeutics, Octant, Jura Bio, Tectonic Therapeutic, and Genentech, and is a co-founder of Seismic Therapeutic. All other authors have no competing interests.

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Extended data

Extended Data Fig. 1 Number of cells per dataset by submission date.

There is a rapid increase in published single-cell perturbation datasets around 2019. We speculate that the slight decrease of dataset numbers after 2021 suggested by the plot is due to the ongoing impact of reduced research in the earlier phases of the COVID-19 pandemic.

Extended Data Fig. 2 Harmonization and analysis workflow.

Perturbation datasets with single-cell molecular profiles with at least two perturbations and one control condition (for example unperturbed) of various modality types were identified in a literature search. Data were obtained from public repositories, and metadata (such as guide identity) from paper supplements. Datasets were reprocessed to standardize annotations and analyzed in parallel. All datasets are now available for download from scperturb.org, along with visualizations and summarizing information.

Extended Data Fig. 3 Pairwise E-distances for NormanWeissman2019 dataset.

E-distances between all pairs of perturbations in the dataset NormanWeissman2019. The color scale is clipped at 5% highest and lowest percentiles. Clusters of similar perturbations are visible, for example a cluster of strongly acting perturbations targeting CEBPA at the top.

Supplementary information

Supplementary Information

Supplementary Fig. 1, Supplemental Note including figures and sections 1–9.

Reporting Summary

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Supplementary Tables 1–5

Supplementary Table 1: Dataset metadata and description of source data papers. Supplementary Table 2: Description of scperturb-formatted gene and cell metadata. Supplementary Table 3: E-statistic results for filtered perturbations across datasets in database. Supplementary Table 4: Drug perturbations appearing in multiple datasets. Supplementary Table 5: Gene perturbations appearing in multiple datasets.

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Peidli, S., Green, T.D., Shen, C. et al. scPerturb: harmonized single-cell perturbation data. Nat Methods 21, 531–540 (2024). https://doi.org/10.1038/s41592-023-02144-y

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  • DOI: https://doi.org/10.1038/s41592-023-02144-y

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