Metadata-Version: 2.1
Name: explosig-data
Version: 0.0.4
Summary: Process mutation data into standard formats originally developed for the ExploSig family of tools
Home-page: https://github.com/lrgr/explosig-data
Author: Leiserson Research Group
License: UNKNOWN
Description: [![Build Status](https://travis-ci.org/lrgr/explosig-data.svg?branch=master)](https://travis-ci.org/lrgr/explosig-data)
        [![PyPI](https://img.shields.io/pypi/v/explosig-data)](https://pypi.org/project/explosig-data/)
        
        ## ExploSig Data
        
        Helpers for processing mutation data into standard formats originally developed for the [ExploSig](https://github.com/lrgr/explosig) family of tools.
        
        - [Documentation](https://lrgr.github.io/explosig-data/)
        
        ### Installation
        
        ```sh
        pip install explosig-data
        ```
        
        ### Example 
        
        With raw SSM/MAF file from ICGC or TCGA:
        
        ```python
        >>> import explosig_data as ed
        
        >>> # Step 1: Process into the ExploSig "standard format":
        >>> data_container = ed.standardize_ICGC_ssm_file('path/to/ssm.tsv') # if ICGC
        >>> data_container = ed.standardize_TCGA_maf_file('path/to/maf.tsv') # if TCGA
        
        >>> # Step 2: Process further
        >>> data_container.extend_df().to_counts_df('SBS_96', ed.categories.SBS_96_category_list())
        
        >>> # Step 3: Access any processed dataframe of interest:
        >>> ssm_df = data_container.ssm_df
        >>> extended_df = data_container.extended_df
        >>> counts_df = data_container.counts_dfs['SBS_96']
        
        
        >>> # Alternatively, use without the chaining API:
        >>> ssm_df = ed.standardize_ICGC_ssm_file('path/to/ssm.tsv', wrap=False) # if ICGC
        >>> ssm_df = ed.standardize_TCGA_maf_file('path/to/maf.tsv', wrap=False) # if TCGA
        >>> extended_df = ed.extend_ssm_df(ssm_df)
        >>> counts_df = ed.counts_from_extended_ssm_df(
                extended_df, 
                category_colname='SBS_96', 
                category_values=ed.categories.SBS_96_category_list()
            )
        ```
        
        With data already in the ExploSig "standard format":
        
        ```python
        >>> import explosig_data as ed
        >>> import pandas as pd
        
        >>> # Step 0: Load the data into a dataframe, for example by reading from a TSV file.
        >>> ssm_df = pd.read_csv('path/to/standard.tsv', sep='\t')
        
        >>> # Step 1: Wrap the dataframe using the container class to allow use of the chainable functions.
        >>> data_container = ed.SimpleSomaticMutationContainer(ssm_df)
        
        >>> # Now see step 2 above (or the alternative steps above).
        ```
        
        
        ### Development
        
        Install for development (in editable mode):
        
        ```sh
        pip install -e .
        ```
        
        Build and push to PyPI:
        
        ```sh
        python setup.py sdist bdist_wheel
        python -m twine upload dist/*
        ```
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
