Metadata-Version: 2.1
Name: came
Version: 0.1.0
Summary: cell-type assignment and gene module extraction of scRNA-seq
Home-page: https://github.com/XingyanLiu/CAME
Author: Xingyan Liu
Author-email: 544568643@qq.com
License: MIT
Description: 
        # CAME
        
        **English** | [简体中文](README_CH.md)
        
        CAME is a tool for **Cell-type Assignment and Module Extraction**, based on a heterogeneous graph neural network.
        
        For detailed usage, please refer to [CAME-Documentation](https://xingyanliu.github.io/CAME/index.html).
        
        <img src="docs/_images/Fig1ABC.png" width="600"/>
        
        CAME outputs the quantitative cell-type assignment for each query cell, that is, 
        the probabilities of cell types that exist in the reference species, which 
        enables the identification of the unresolved cell states in the query data. 
        
        Besides, CAME gives the aligned cell and gene embeddings across species, which 
        facilitates low-dimensional visualization and joint gene module extraction.
        
        <img src="docs/_images/Fig1D.png" width="600"/>
        
        
        ### Installation
        
        It's recommended to create a conda environment for running CAME:
        
        ```shell
        conda create -n came python=3.8
        conda activate came
        ```
        
        Install required packages:
        
        ```shell
        pip install "scanpy[leiden]"
        pip install torch  # >=1.8 
        pip install dgl  
        ```
        
        See [scanpy](https://scanpy.readthedocs.io/en/stable/), 
        [PyTorch](https://pytorch.org/) and [DGL](https://www.dgl.ai/) 
        for detailed installation guide (especially for GPU version).
        
        Install CAME from source code:
        
        ```shell
        git clone https://github.com/XingyanLiu/CAME.git
        cd CAME
        python setup.py install
        ```
        
        ### Example data
        
        The test code is based on the sample data attached to the CAME package. 
        It is initially saved in compressed form (`CAME/came/sample_data.zip`),
        and will be automatically decompressed to the default directory 
        (`CAME/came/sample_data/`) when necessary, which contains the following files:
        
        - gene_matches_1v1_human2mouse.csv (optional)
        - gene_matches_1v1_mouse2human.csv (optional)
        - gene_matches_human2mouse.csv
        - gene_matches_mouse2human.csv
        - raw-Baron_mouse.h5ad
        - raw-Baron_human.h5ad 
        
        If you want to apply CAME to analyze your own datasets, you need to 
        prepare at least the last four files for cross-species analysis, and 
        only the last two files for the same species (e.g., cross-dataset integration).
        
        > NOTE:
        > the file `raw-Baron_human.h5ad` is a subsample from the original data 
        > for code testing. The resulting annotation accuracy may not be as good as 
        > using the full dataset as the reference.
        
        ### Test CAME's pipeline (optional)
        
        To test the package, run the python file `test_pipeline.py`:
        
        ```shell
        python test_pipeline.py 
        ```
        
        ### Contribute
        
        * Issue Tracker: https://github.com/XingyanLiu/CAME/issues
        * Source Code:
          * https://github.com/zhanglabtools/CAME
          * https://github.com/XingyanLiu/CAME (the developmental version)
        
        ### Support
        
        If you are having issues, please let us know. We have a mailing list located at:
        
        * xingyan@amss.ac.cn
        * 544568643@qq.com
        
        
        ### Citation
        
        If CAME is useful for your research, consider citing our preprint:
        
        > Cross-species cell-type assignment of single-cell RNA-seq by a heterogeneous graph neural network.
           Xingyan Liu, Qunlun Shen, Shihua Zhang.
           bioRxiv 2021.09.25.461790; doi: https://doi.org/10.1101/2021.09.25.461790
        
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.8.0
Description-Content-Type: text/markdown
