cohorts 0.1.1

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
--------------

::

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])

.. |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