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)