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

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ExploSig Data

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


pip install explosig-data


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/ssm.tsv') # if TCGA

>>> # Step 2: Process further
>>> data_container.extend_df().to_counts_df('SBS_96', ed.categories.SBS_96_category_list()))

>>> # Step 3: Use the processed dataframe of interest.
>>> 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(

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).


Build and install from the current directory.

python sdist bdist_wheel && pip install .

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