cohorts 0.0.5

Utilities for analyzing mutations and neoepitopes in patient cohorts

`|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