Accurate cell type annotation for single-cell chromatin accessibility data via contrastive learning and reference guidance
Installation
Install RAINBOW from PYPI
pip install scrainbow
You can also install RAINBOW from GitHub via
git clone git://github.com/BioX-NKU/RAINBOW.git
cd RAINBOW
python setup.py install
The dependencies will be automatically installed along with RAINBOW.
Quick Start
Input:
h5ad file Files from the training set scCAS data and files from the scCAS data that need to be annotated.
Output:
pred_labels: Array object which contains cell type annotation results.
Using tutorial:
import scrainbow as rainbow
pred_labels = rainbow.run(train_path,test_path)
If there is reference data can be incorporated, you can get annotation results via
pred_labels = rainbow.run(train_path,test_path,refer_path,refer=True)
If you want to identify the novel type:
pred_labels = rainbow.run(train_path,test_path,pred_novel=True)