SuperSCC (Super single cell clustering), a novel algorithm that utilizes machine-learning models to discover cell identities and gene modules from multiple datasets without the need of data integration. Of note, SuperSCC can be implemented both in cell lineage and cell state level, thereby building the hierarchy of cell programs with specific cell identity and gene modules. Such information has the great potential to identify the shared rare populations across datasets regardless of batch effect and benefits label transfer for mapping cell labels from reference to query.
To install SuperSCC, you should run the following code if you already get SuperSCC's tar ball
pip install SuperSCC.tar.gz
Usually, all dependencies should be downloaded and installed automatically.
To install SuperSCC via GitHub, you can do::
git clone https://github.com/tf1993614/SuperSCC/
cd SuperSCC
python setup.py install
After installing correctly, you can load SuperSCC
import SuperSCC as scc
Since the gene module-relevant functions was also written in R, you can also do the gene module analysis in R environment aftering installing geneModule R package.
R
install.packages("geneModule_0.1.tar.gz", repos = .libPaths()[1], type = "source")
.libPaths()[1] # get the location where packages are installed
q()
For how to use SuperSCC, please read the SuperSCC's documentation.
Tang, F., Zhang, Z., Zhou, W. et al. Unveiling gene modules at Atlas scale through hierarchical clustering of single-cell data. Nat Commun (2026). https://doi.org/10.1038/s41467-026-73054-8