Metadata-Version: 2.1
Name: fast-kernel-set-test
Version: 0.1.0
Summary: This is the package for various epistasis related softwares.
Home-page: https://github.com/sriramlab/FastKAST
Author: Boyang Fu
Author-email: fbyang1995@gmail.com
License: UNKNOWN
Description: <a href="https://zenodo.org/badge/latestdoi/429674106"><img src="https://zenodo.org/badge/429674106.svg" alt="DOI"></a>
        
        <img src="FastKAST.png" alt="icon" width="100"/>
        
        # Fast Model-X Kernel-based Set Testing Toolkits
        
        This folder has been updated with both the [FastKAST](https://www.nature.com/articles/s41467-023-40346-2) and [QuadKAST](https://genome.cshlp.org/content/early/2024/08/29/gr.279140.124)
        
        Please check sub-branch for detailed instruction on each specific method. 
        
        
        ## Requirements
        1. You need python >= 3.60 in order to run the code (anaconda3 recommended)
        2. `pip install .`
        
        You can either follow the standard pipeline `FastKAST_annot.py` and `QuadKAST_annot.py`, or import the neccessary function to build based on your own I/O.
        
        ## Exmaple
        To run the demo FastKAST code with a customized window size, you can generate a annotation file with "start_index end_index" as a row, and run
        ```
        python FastKAST_annot.py --bfile ./example/sim --phen ./example/sim.pheno --annot ./example/sim.new.annot
        ```
        Or directly run
        ```
        sh run_rbf_annot.sh
        ```
        
        To run the demo FastKAST code with a customized window size, you can generate a annotation file with "start_index end_index" as a row, and run
        ```
        python QuadKAST_annot.py --bfile ./example/sim --phen ./example/sim.pheno --annot ./example/sim.new.annot
        ```
        Or directly run
        ```
        sh run_quad_annot.sh
        ```
        
        ## Data availability
        The detailed statistics used to generate the main table and the Venn diagram of the paper are provided in the `Data` folder
        
        ✅ Efficient multi-traits analysis (Sep 30, 2024)
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
