This repository includes a Jupyter notebook to execute a set of standard questions and figures that provide a descriptive summary of the clinical interpretation landscape for a given cohort, given that each profile has been processed with the Molecular Oncology Almanac.
You can open moalmanac-cohort.ipynb and configure it to run with your own data by following the instructions to edit variables under the Load data and Configuration sections.
Currently, the notebook uses the actionable.txt and somatic.scored.txt outputs from the Molecular Oncology Almanac.
This repository comes packaged with interpretations for 100 patients with kidney papillary cell carcinoma (KIRP) from TCGA, as analyzed in a previous study.
The notebook currently addresses the following questions,
- How many patients are contained within this cohort?
- How many patients have genomic alterations?
- How many alterations does each patient have, by feature type?
- How many patients have at least one alteration assocaited with therapeutic sensitivity? resistance? disease prognosis?
- What percentage of patients have an association for therapeutic sensitivity per evidence type? What about cumulatively?
- What are the most common observed alterations, by feature type?
- What are the most common observed clinically relevant alterations, by feature type?
- What are the most common observed biologically relevant alterations, by feature type?
- What therapies are most commonly highlighted for therapeutic sensitivity? For resistance?
- Which alteration and therapy pairs are most commonly highlighted for therapeutic sensitivity? For resistance?
And the notebook also generates seven figures,
- Patients with clinically relevant alterations, by feature type
- Associations by evidence for therapeutic sensitivity, resistance, and disease prognosis
- Counts of patients by evidence with at least one alteration associated with therapeutic sensitivity, resistance, and disease prognosis
To use this repository, please follow these instructions to download this repository from GitHub and install python dependencies by setting up a virtual environment.
This package can be downloaded through Github on the website or by using terminal. To download on the website, navigate to the top of this page, click the green Clone or download button, and select Download ZIP. This will download this repository in a compressed format. To install using Github on terminal, type
git clone https://github.com/vanallenlab/moalmanac-cohort.git
cd moalmanac-cohortCode in this repository uses Python 3.10 and we recommend using a virtual environment and running Python with either Anaconda or Miniconda. After installing Anaconda or Miniconda, you can set up by running
conda create -y -n moalmanac-cohort python=3.10
conda activate moalmanac-cohort
pip install -r requirements.txt
ipython kernel install --user --name=moalmanac-cohortIf you are using base Python, you can create a virtual environment and install dependencies by running:
virtualenv moalmanac-cohort
source activate moalmanac-cohort/bin/activate
pip install -r requirements.txtIf you find this tool or any code herein useful, please cite:
DIAGNOSTIC AND CLINICAL USE PROHIBITED. DANA-FARBER CANCER INSTITUTE (DFCI) and THE BROAD INSTITUTE (Broad) MAKE NO REPRESENTATIONS OR WARRANTIES OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NONINFRINGEMENT OR VALIDITY OF ANY INTELLECTUAL PROPERTY RIGHTS OR CLAIMS, WHETHER ISSUED OR PENDING, AND THE ABSENCE OF LATENT OR OTHER DEFECTS, WHETHER OR NOT DISCOVERABLE.
In no event shall DFCI or Broad or their Trustees, Directors, Officers, Employees, Students, Affiliates, Core Faculty, Associate Faculty and Contractors, be liable for incidental, punitive, consequential or special damages, including economic damages or injury to persons or property or lost profits, regardless of whether the party was advised, had other reason to know or in fact knew of the possibility of the foregoing, regardless of fault, and regardless of legal theory or basis. You may not download or use any portion of this program for any non-research use not expressly authorized by DFCI or Broad. You further agree that the program shall not be used as the basis of a commercial product and that the program shall not be rewritten or otherwise adapted to circumvent the need for obtaining permission for use of the program other than as specified herein.