cohorts 0.4.2

Utilities for analyzing mutations and neoepitopes in patient cohorts

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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>`__:

.. code:: bash

pip install cohorts

Features
--------

- Data management: construct a ``Cohort`` consisting of ``Patient``\ s
with ``Sample``\ s.
- Use ``varcode`` and ``topiary`` to generate and cache variant effects
and predicted neoantigens.
- Provenance: track the state of the world (package and data versions)
for a given analysis.
- Aggregation functions: built-in functions such as
``missense_snv_count``, ``neoantigen_count``,
``expressed_neoantigen_count``; or create your own functions.
- Plotting: survival curves via ``lifelines``, response/no response
plots (with Mann-Whitney and Fisher's Exact results), ROC curves.
Example: ``cohort.plot_survival(on=missense_snv_count, how="pfs")``.
- Filtering: filter collections of variants/effects/neoantigens by, for
example, variant statistics.
- Pre-define data sets to work with. Example:
``cohort.as_dataframe(join_with=["tcr", "pdl1"])``.

In addition, several other libraries make use of ``cohorts``: \*
`pygdc <http://github.com/hammerlab/pygdc>`__ \*
`query\_tcga <http://github.com/jburos/query_tcga>`__

Quick Start
-----------

One way to get started using Cohorts is to use it to analyze TCGA data.

As an example, we can create a cohort using
`query\_tcga <http://github.com/jburos/query_tcga>`__:

.. code:: python

from query_tcga import cohort, config

# provide authentication token
config.load_config('config.ini')

# load patient data
blca_patients = cohort.prep_patients(project_name='TCGA-BLCA',
project_data_dir='data')

# create cohort
blca_cohort = cohort.prep_cohort(patients=blca_patients,
cache_dir='data-cache')

Then, use ``plot_survival()`` to summarize a potential biomarker (e.g.
``snv_count``) by survival:.

.. code:: python

from cohorts.functions import snv_count
blca_cohort.plot_survival(snv_count, how='os', threshold='median')

Which should produce a summary of results including this plot:

.. figure:: /docs/survival_plot_example.png
:alt: Survival plot example

Survival plot example

We could alternatively use ``plot_benefit()`` to summarize OS>12mo
instead of survival:

.. code:: python

blca_cohort.plot_benefit(snv_count)

.. figure:: /docs/benefit_plot_example.png
:alt: Benefit plot example

Benefit plot example

See the full example in the `quick-start
notebook <http://nbviewer.jupyter.org/github/hammerlab/tcga-blca/blob/master/Quick-start%20-%20using%20Cohorts%20with%20TCGA%20data.ipynb>`__

Building from Scratch
---------------------

.. code:: python

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")

.. code:: python

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]
)

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