Metadata-Version: 2.1
Name: fast-kernel-set-test
Version: 0.1.1
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
        https://pypi.org/project/fast-kernel-set-test/0.1.0/
        
        
        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. 
        
        # Table of contents:
        1. [Installation](##Installation) 
        2. [Basic usage](##Basic_usage) 
            1. [FastKAST](###FastKAST)
            2. [QuadKAST](###QuadKAST)
        3. [Useful functions](##Functions)
        
        
        ## Installation <a name="Installation"></a>
        1. You need python >= 3.60 in order to run the code (anaconda3 recommended)
        2. `pip install fast-kernel-set-test` or install from source
        
        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.
        
        ## Basic usage <a name="Basic_usage"></a>
        
        ### FastKAST <a name="FastKAST"></a>
        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
        ```
        
        ### QuadKAST <a name="QuadKAST"></a>
        To run the demo QuadKAST 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
        ```
        
        ## Useful functions <a name="Functions"></a>
        * Single trait analysis
        ```python
        ## Given covariates c: (NxM), input Z: (NxD), and output y: (Nx1)
        from FastKAST import getfullComponentPerm
        results = getfullComponentPerm(c,Z,y,Perm=10)
        ## results: {'pval': [obs_pval, perm_pval1, ..., perm_pval10]}     
        ```
        * Multi-traits analysis
        ```python
        ## Given covariates c: (NxM), input Z: (NxD), and output y: (NxK)
        from FastKAST import getfullComponentMulti
        results = getfullComponentMulti(c,Z,y)
        ## results: {'pval': [obs_pval1, obs_pval2, ..., obs_pvalK]}     
        ```
        
        ## 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
