Metadata-Version: 1.1
Name: cohorts
Version: 0.0.5
Summary: Utilities for analyzing mutations and neoepitopes in patient cohorts
Home-page: https://github.com/tavinathanson/cohorts
Author: Tavi Nathanson
Author-email: tavi {dot} nathanson {at} gmail {dot} com
License: http://www.apache.org/licenses/LICENSE-2.0.html
Description: `|Build Status| <https://travis-ci.org/hammerlab/cohorts>`_ `|Coverage
        Status| <https://coveralls.io/github/hammerlab/cohorts?branch=master>`_
        
        Cohorts
        =======
        
        Cohorts is a library for analyzing and plotting clinical data, mutations
        and neoepitopes in patient cohorts.
        
        It calls out to external libraries like
        `topiary <https://github.com/hammerlab/topiary>`_ and caches the results
        for easy manipulation.
        
        Installation
        ------------
        
        You can install Cohorts using
        `pip <https://pip.pypa.io/en/latest/quickstart.html>`_:
        
        ::
        
            pip install cohorts
        
        Usage Examples
        --------------
        
        ::
        
            cohort = Cohort(
                data_dir="/my/input/data",
                cache_dir="/where/cohorts/results/get/saved",
                sample_ids=["sample_1", "sample_2"],
                clinical_dataframe=pandas_dataframe_with_clinical_data,
                clinical_dataframe_id_col="sample_id_in_dataframe",
                os_col="Overall Survival",
                pfs_col="Progression-Free Survival",
                deceased_col="deceased",
                progressed_or_deceased_col="progressed_or_deceased"
            )
        
            cohort.plot_survival(how="os")
        
        ::
        
            def mutect_snv_file_format_func(sample_id, normal_bam_id, tumor_bam_id):
                return "Mutect-%d-normal=%s.bam-tumor=%s.bam-merged.vcf" % (
                    sample_id, normal_bam_id, tumor_bam_id)
        
            def strelka_snv_file_format_func(...):
                ...
        
            cohort = Cohort(
                ...
                benefit_col="patient_durable_benefit",
                snv_file_format_funcs=[
                    mutect_snv_file_format_func,
                    strelka_snv_file_format_func
                ]
            )
        
            # Comparison plot of missense mutation counts between benefit and no-benefit patients
            cohort.plot_benefit(on=missense_snv_count)
        
            # Raw missense mutations counts
            missense_snv_col, updated_dataframe = missense_snv_count(cohort)
        
        .. |Build
        Status| image:: https://travis-ci.org/hammerlab/cohorts.svg?branch=master
        .. |Coverage
        Status| image:: https://coveralls.io/repos/hammerlab/cohorts/badge.svg?branch=master&service=github
        
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Programming Language :: Python
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
