Single Cell Perturbation Datasets

scPerturb.org is a resource of standardized datasets reporting targeted perturbations with single-cell readouts and to facilitate the development and benchmarking of computational approaches in system biology.

Read more and cite scPerturb

scPerturb: harmonized single-cell perturbation data Stefan Peidli, Tessa Durakis 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 Nature Methods 2024; doi: 10.1038/s41592-023-02144-y

Data Explorer

Explore dataset properties here (e.g., links to publications, perturbation type, perturbation counts, etc.)

Data

• RNA-seq datasets: zenodo.org/doi/10.5281/zenodo.7041848
• ATAC-seq: zenodo.org/doi/10.5281/zenodo.7058381
• Dataset information: bioRxiv Supplementary Table 1

Source Code

• Source code and bug reports on GitHub
• scPerturb Python Package for the use of E-distance to compare and statistically test how much cells in high-dimensional singe-cell data differ (e.g., untreated vs treated)
• scPerturbR: scperturb for R and Seurat

Funding and Acknowledgements

National Resource for Network Biology (NRNB, P41GM103504) ● Supported by the Wellcome Leap ∆Tissue Program ● Deutsche Forschungsgemeinschaft (DFG, RTG2424 CompCancer) ● Einstein Stiftung Berlin (Einstein visiting fellow program) ● Computation was in part performed on the HPC for Research cluster of the Berlin Institute of Health