Metadata-Version: 1.1
Name: cohorts
Version: 0.3.0
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: `|PyPI| <>`_ `|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
        --------------
        
        ::
        
            patient_1 = Patient(
                id="patient_1",
                os=70,
                pfs=24,
                deceased=True,
                progressed=True,
                benefit=False
            )
        
            patient_2 = Patient(
                id="patient_2",
                os=100,
                pfs=50,
                deceased=False,
                progressed=True,
                benefit=False
            )
        
            cohort = Cohort(
                patients=[patient_1, patient_2],
                cache_dir="/where/cohorts/results/get/saved"
            )
        
            cohort.plot_survival(on="os")
        
        ::
        
            sample_1_tumor = Sample(
                is_tumor=True,
                bam_path_dna="/path/to/dna/bam",
                bam_path_rna="/path/to/rna/bam"
            )
        
            patient_1 = Patient(
                id="patient_1",
                ...
                snv_vcf_paths=["/where/my/mutect/vcfs/live",
                               "/where/my/strelka/vcfs/live"]
                indel_vcfs_paths=[...],
                tumor_sample=sample_1_tumor,
                ...
            )
        
            cohort = Cohort(
                ...
                patients=[patient_1]
            )
        
            # 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, dataframe = cohort.as_dataframe(missense_snv_count)
            (col_1, col_2), dataframe = cohort.as_dataframe([missense_snv_count, neoantigen_count])
        
        .. |PyPI| image:: https://img.shields.io/pypi/v/cohorts.svg?maxAge=2592000
        .. |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
