hammerlab/cohorts

Name: cohorts

Owner: Hammer Lab

Description: Utilities for analyzing mutations and neoepitopes in patient cohorts

Created: 2016-03-14 15:52:59.0

Updated: 2017-11-02 02:56:44.0

Pushed: 2017-12-30 21:03:49.0

Homepage: null

Size: 560

Language: Python

GitHub Committers

User	Most Recent Commit	# Commits

Other Committers

User	Email	Most Recent Commit	# Commits

README

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 and caches the results for easy manipulation.

Cohorts requires Python 3 (3.3+). We are no longer maintaining compatability with Python 2. For context, see this Python 3 statement.

Installation

You can install Cohorts using pip:

install cohorts

Features

• Data management: construct a Cohort consisting of Patients with Samples.
• 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
• 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:

 query_tcga import cohort, config

ovide authentication token
ig.load_config('config.ini')

ad patient data
_patients = cohort.prep_patients(project_name='TCGA-BLCA',
                                 project_data_dir='data')

eate cohort
_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:.

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

Which should produce a summary of results including this plot:

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

_cohort.plot_benefit(snv_count)

See the full example in the quick-start notebook

Building from Scratch

ent_1 = Patient(
id="patient_1",
os=70,
pfs=24,
deceased=True,
progressed=True,
benefit=False


ent_2 = Patient(
id="patient_2",
os=100,
pfs=50,
deceased=False,
progressed=True,
benefit=False


rt = Cohort(
patients=[patient_1, patient_2],
cache_dir="/where/cohorts/results/get/saved"


rt.plot_survival(on="os")

ython
le_1_tumor = Sample(
is_tumor=True,
bam_path_dna="/path/to/dna/bam",
bam_path_rna="/path/to/rna/bam"


ent_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,
...


rt = Cohort(
...
patients=[patient_1]