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:

train_set AnnData object of shape n_obs × n_varswith cell type labels. Rows correspond to cells and columns to genes.

test_set AnnData object of shape n_obs × n_vars without cell type labels. Rows correspond to cells and columns to genes.

Output:

pred_labels: Array object which contains cell type annotation results.

Using tutorial:

import scrainbow as rainbow
pred_labels = rainbow.run(train_set,test_set)

If there is reference data (AnnData object) can be incorporated, you can get annotation results via

pred_labels = rainbow.run(train_set,test_set,refer_set)

If you want to identify the novel type:

pred_labels = rainbow.run(train_set,test_set,pred_novel=True)

In this situation, the identified novel-type cells will be annotated as "novel".

The source datasets are available at here.