Clustering estrogen receptor-positive breast cancer tumors based on hormonal response type

Project in Machine Learning (CS-433)

EPFL, 2019

Abstract

We employ unsupervised machine learning techniques to cluster subtypes of estrogen receptor-positive breast cancer, which is the most common variant worldwide. Clustering is done according to hormone responses obtained from in vivo models of patient-derived xenografts. Our results facilitate more targeted treatment of patients, responding to the urgent need for personalized medicine to treat breast cancer.

• Read the report
• Read the data analysis
• Read the cluster analysis

Architecture

• results/ directory is for output and plots
• scripts/ directory contains all the code
• constants.py includes constants used throughout the project
• helpers.py includes various helper functions
• load.py includes functions for loading and manipulating the data
• plots.py includes plotting functions
• clustering_helpers.py includes helper functions used for the cluster analysis
• data_analysis.ipynb is the Jupyter Notebook file that includes the exploratory data analysis and visualizations
• cluster_analysis.ipynb is the Jupyter Notebook file that includes the cluster analysis

Dependencies

• Data handling and ML libraries:
  • sklearn (v. 0.21.3)
  • pandas (v. 0.25.1)
  • numpy (v. 1.16.5)
• Plotting libraries:
  • matplotlib
  • seaborn
  • plotly and chart_studio

Data

Download here

Reproduction

1. Clone or fork the repository
2. Download the data and add the data/ folder to the root of the project
3. Install Jupyter Notebook
4. Install the abovementioned libraries
5. Run data_analysis.ipynb to reproduce the data analysis results
6. Run cluster_analysis.ipynb to reproduce the cluster analysis results

Authors

• Lisa Dratva, lisa.dratva@epfl.ch
• Michal Pleskowicz, michal.pleskowicz@epfl.ch
• Valentin Oliver Loftsson, valentin.loftsson@epfl.ch

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments

We thank Fabio De Martino, our supervisor at the BRISKEN lab, for his constant guidance and support throughout the learning process.