ghost-cell-busters

Notebooks

This folder contains Jupyter notebooks related to the Ghost Cell Busters project. The notebooks are organized to facilitate exploration, experimentation, and documentation of the project’s data analysis, modeling, and results.

Structure

Data Processing and Feature Engineering

01_GSE176078.ipynb
Initial data loading, preprocessing, and feature engineering for the GSE176078 training dataset.
04_GSE161529_obs.ipynb
Data processing and observation file generation for the GSE161529 validation dataset.
07_GSE180286_obs.ipynb
Data processing and observation file generation for the GSE180286 validation dataset.

Model Development and Interpretation

02_GSE176078_model.ipynb
XGBoost model training, validation, and performance evaluation using GSE176078 dataset.
03_GSE176078_shap.ipynb
SHAP analysis for model interpretability and feature importance visualization.

Cross-Dataset Validation

05_GSE161529_cross_validation.ipynb
Cross-validation of trained model on GSE161529 dataset with tumor prediction generation.
08_GSE180286_cross_validation.ipynb
Cross-validation of trained model on GSE180286 dataset with tumor prediction generation.

Biological Analysis

06_GSE161529_oncogene.ipynb
Oncogene expression analysis, differential expression testing, and pathway visualization for GSE161529.
09_GSE180286_oncogene.ipynb
Oncogene expression analysis, differential expression testing, and pathway visualization for GSE180286.

Comparative Analysis

10_Combined_DE.ipynb
Combined differential expression analysis across datasets with drug target identification and clinical relevance assessment.

Usage

Clone the repository and navigate to this folder.
Open notebooks using JupyterLab or Jupyter Notebook.
Follow the instructions in each notebook for reproducibility.

Notes

Notebooks are organized by topic and stage of the project.
Please refer to individual notebook headers for specific details and requirements.
For questions or contributions, see the main project README.