Skip to content

SCOTCH is a Single-Cell multi-modal integration method leveraging the Optimal Transport algorithm and a cell matCHing strategy

License

Notifications You must be signed in to change notification settings

ZJUFanLab/SCOTCH

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

12 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

SCOTCH v1.0.0

Cross-modal matching and integration of single-cell multi-omics data

Penghui Yang, Kaiyu Jin, Lijun Jin, ..., Xiaohui Fan*

SCOTCH is a computational method that leverages the optimal transport algorithm and a cell matching strategy to integrate scRNA-seq and scATAC-seq data. SCOTCH takes into account the adverse effects of cell type abundance and cell number differences on data integration during the calculation process, and predicts cell pairing relationships to meet the needs of downstream in-depth analysis.

Image text

Installation of SCOTCH

pot 0.8.2 numpy 1.22.4 pandas 1.4.3 scikit-learn 1.2.0 scipy 1.8.1 scanpy 1.9.1 anndata 0.7.5 igraph 0.10.8 louvain 0.7.1 matplotlib 3.5.2


pip install scotch-sc

Tutorials

We have applied SCOTCH on different tissues of multiple species, here we give step-by-step tutorials for application scenarios. And datasets in .h5ad fomat can be downloaded from Google Drive.

About

Should you have any questions, please feel free to contact the author of the manuscript, Mr. Penghui Yang (yangph@zju.edu.cn).

References

About

SCOTCH is a Single-Cell multi-modal integration method leveraging the Optimal Transport algorithm and a cell matCHing strategy

Topics

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published