MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov-Arnold network and data augmentation

Installation

It's prefered to create a new environment for MultiKano:

conda create -n MultiKano python==3.7
conda activate MultiKano

MultiKano is available on PyPI, and could be installed using:

pip install MultiKano

Installation via Github is also provided

git clone https://github.com/Biox-NKU/MultiKano
cd MultiKano
pip install multikano-0.1.0-py3-none-any.whl

This process will take approximately 5 to 10 minutes, depending on the user's computer device and internet connectivition.

Quick Start

Input:

Train_set_RNA: AnnData object of shape n_obs × n_vars with cell_type labels. Rows correspond to cells and columns to genes.

Train_set_ATAC: AnnData object of shape n_obs × n_vars with cell_type labels. Rows correspond to cells and columns to peaks.

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

Test_set_ATAC: AnnData object of shape n_obs × n_vars without cell type labels. Rows correspond to cells and columns to peaks.

Note that Train_set_RNA and Train_set_ATAC should be paired, and the same applies to Test_set_RNA and Test_set_ATAC.

Output:

Pred_labels: Array object which contains cell type annotation results on the test set.

Using tutorial:

import multikano as multikano

Pred_labels = multikano.run_model(Train_set_RNA, Train_set_ATAC, Test_set_RNA, Test_set_ATAC)