obifarin/DKO-lipidomics

Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer

DKO-lipidomics

Code base for the study: "Machine Learning Reveals Lipidome Remodeling Dynamics in a Mouse Model of Ovarian Cancer."

Link to Preprint

Requirements

• Anaconda
• Python >= 3.8.8
• Jupyter

1. git repository

Clone local copy of git repository

git clone https://github.com/obifarin/DKO-lipidomics

(or use a git GUI client of your choice)

2. Environment setup and access jupyter notebooks

Setup python environment (dko-gatech.yml) in the terminal.

(or use anaconda GUI.)

3. Notebook contents

Name	Description
1_global_lipidomic_changes.ipynb	Global significant changes: univariate statistical testing and unsupervised learning.
2_lipidomic_trajectory_clusters.ipynb	Lipidome alterations in response to ovarian cancer progression.
3_machine_learning_DKO_classification.ipynb	Time-resolved machine learning discriminates tumor stages of HGSC in DKO mice.
4_survival_analysis_prognostics.ipynb	Prognostic circulating lipids in DKO mice.
5_ML_DKO_permutation_test.ipynb	Permutation tests for ML model validation.