altoslabs/perturbench

The dataset contains data used in work: "Perturbench: Benchmarking machine learning models for cellular perturbation analysis."

The data comes from the following publications:

• Norman, T. M., Horlbeck, M. A., Replogle, J. M., Ge, A. Y., Xu, A., Jost, M., Gilbert, L. A., and Weissman, J. S. (2019). Exploring genetic interaction manifolds constructed from rich single-cell phenotypes. Science, 365(6455):786–793.
• Srivatsan, S. R., McFaline-Figueroa, J. L., Ramani, V., Saunders, L., Cao, J., Packer, J., Pliner, H. A., Jackson, D. L., Daza, R. M., Christiansen, L., Zhang, F., Steemers, F., Shendure, J., and Trapnell, C. (2020). Massively multiplex chemical transcriptomics at single-cell resolution. Science, 367(6473):45–51.
• Frangieh, C. J., Melms, J. C., Thakore, P. I., Geiger-Schuller, K. R., Ho, P., Luoma, A. M., Cleary, B., Jerby-Arnon, L., Malu, S., Cuoco, M. S., Zhao, M., Ager, C. R., Rogava, M., Hovey, L., Rotem, A., Bernatchez, C., Wucherpfennig, K. W., Johnson, B. E., Rozenblatt-Rosen, O., Schadendorf, D., Regev, A., and Izar, B. (2021). Multimodal pooled Perturb-CITE-seq screens in patient models define mechanisms of cancer immune evasion. Nat. Genet., 53(3):332–341.
• Jiang, L., Dalgarno, C., Papalexi, E., Mascio, I., Wessels, H.-H., Yun, H., Iremadze, N., Lithwick Yanai, G., Lipson, D., and Satija, R. (2024a). Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens. bioRxiv, page 2024.01.29.576933.
• McFaline-Figueroa, J. L., Srivatsan, S., Hill, A. J., Gasperini, M., Jackson, D. L., Saunders, L., Domcke, S., Regalado, S. G., Lazarchuck, P., Alvarez, S., et al. (2024). Multiplex single cell chemical genomics reveals the kinase dependence of the response to targeted therapy. Cell Genomics, 4(2)
• Szałata, A., Benz, A., Cannoodt, R., Cortes, M., Fong, J., Kuppasani, S., Lieberman, R., Liu, T., Mas-Rosario, J. A., Meinl, R., Nourisa, J., Tumiel, J., Tunjic, T. M., Wang, M., Weber, N., Zhao, H., Anchang, B., Theis, F. J., Luecken, M. D., Burkhardt, D. B. (2024). A benchmark for prediction of transcriptomic responses to chemical perturbations across cell types. NeurIPS, (38).

Datasets with fixed splits have their splits included as .csv files with two colums: the first corresponds to the cell ID (which is the .obs_names of the respective h5ad file) and second to the split value (train, val, test). Some datasets contain multiple splits in which case the split files are in a tar.gz.