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Process mutation data into standard formats originally developed for the ExploSig family of tools

Project description

Build Status PyPI

ExploSig Data

Helpers for processing mutation data into standard formats originally developed for the ExploSig family of tools.

Installation

pip install explosig-data

Example

With raw SSM/MAF file from ICGC or TCGA:

>>> 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":

>>> 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

Build and install from the current directory.

python setup.py sdist bdist_wheel && pip install .

Project details


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