cohorts 0.1.0

Utilities for analyzing mutations and neoepitopes in patient cohorts

[](https://travis-ci.org/hammerlab/cohorts) [](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):

```bash
pip install cohorts
```

Usage Examples
--------------

```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(how="os")
```

```python
sample_1_tumor = Sample(
id="sample_1_tumor",
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 = missense_snv_count(cohort)
```