Metadata-Version: 2.1
Name: scTenifoldpy
Version: 0.1.1
Summary: scTenifoldpy
Home-page: https://github.com/qwerty239qwe/scTenifoldpy
Author: Yu-Te Lin
Author-email: qwerty239qwe@gmail.com
License: MIT
Description: # scTenifoldpy
        [![PyPI pyversions](https://img.shields.io/pypi/pyversions/biodbs.svg)](https://pypi.python.org/pypi/biodbs/)
        [![Pattern](https://img.shields.io/badge/DOI-10.1016/j.patter.2020.100139-blue)](https://www.sciencedirect.com/science/article/pii/S2666389920301872#bib48)
        [![GitHub license](https://img.shields.io/github/license/qwerty239qwe/scTenifoldpy.svg)](https://github.com/qwerty239qwe/scTenifoldpy/blob/master/LICENSE)
        
        This package is a Python version of [scTenifoldNet](https://github.com/cailab-tamu/scTenifoldNet) 
        and [scTenifoldKnk](https://github.com/cailab-tamu/scTenifoldKnk). If you are a R/MATLAB user, 
        please install them to use their functions. 
        Also, please [cite](https://www.sciencedirect.com/science/article/pii/S2666389920301872) the original paper properly 
        if you are using this in a scientific publication. Thank you!
        
        ### Installation
        ```
        pip install scTenifoldpy
        ```
        
        
        ### Usages
        scTenifold can be imported as a normal Python package:
        #### scTenifoldNet
        ```python
        from scTenifold.data import get_test_df
        from scTenifold import scTenifoldNet
        
        df_1, df_2 = get_test_df(n_cells=1000), get_test_df(n_cells=1000)
        sc = scTenifoldNet(df_1, df_2, "X", "Y", qc_kws={"min_lib_size": 10})
        result = sc.build()
        ```
        
        #### scTenifoldKnk
        ```python
        from scTenifold.data import get_test_df
        from scTenifold import scTenifoldKnk
        
        df = get_test_df(n_cells=1000)
        sc = scTenifoldKnk(data=df,
                           ko_method="default",
                           ko_genes=["NG-1"],  # the gene you wants to knock out
                           qc_kws={"min_lib_size": 10, "min_percent": 0.001},
                           )
        result = sc.build()
        ```
        
        ### Command Line tool
        Once the package is installed, users can use commandline tool to generate all the results <br>
        Use this command to create a config.yml file, 
        ```shell
        python -m scTenifold config -t 1 -p ./net_config.yml
        ```
        Next, open the config file, add data path, and edit the parameters.<br>
        Then use the command below to produce the scTenifoldNet results:
        ```shell
        python -m scTenifold net -c ./net_config.yml -o ./output_folder
        ```
        
        Or use the command below to produce the knockout results:
        ```shell
        python -m scTenifold knk -c ./knk_config.yml -o ./output_folder
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Description-Content-Type: text/markdown
