For the publication see: Peidli, S., Green, T.D., et al. Nature Methods (2024).
The datasets are available to download on scperturb.org (where you can also find an interactive table of all included datasets). The latest versions are available in full on Zenodo, depending on the modality you are interested in:
A python package to compute E-distances in single-cell perturbation data and perform E-tests.
Just install via pip:
pip install scperturb
Check out this notebook for a tutorial. Basic usage is:
# E-distances
estats = edist(adata, obs_key='perturbation')
# E-distances to a specific group (e.g. 'control')
estats_control = estats.loc['control']
# E-test for difference to control
df = etest(adata, obs_key='perturbation', obsm_key='X_pca', dist='sqeuclidean', control='control', alpha=0.05, runs=100)
We wrote an R version of scperturb that works with Seurat objects. You can find it as scperturbR on CRAN. A basic usage vignette is WIP. Install using:
install.packages('scperturbR')
Instructions to run the code to reproduce the figures and tables in the paper and supplement: