Tutorials Overview¶

This section provides comprehensive tutorials for using renalprog to analyze kidney cancer progression. Each tutorial is designed to be self-contained yet builds upon previous steps.

Tutorial Structure¶

🚀 Quick Start¶

New to renalprog? Start here!

Quick Start: 10-minute introduction to core functionality
Using Pretrained Models: Fastest way to reproduce paper results
Data Requirements: Understanding input data formats

📋 Complete Pipeline¶

Step-by-step walkthrough of the entire analysis pipeline:

Data Processing: Download, filter, and preprocess TCGA data
VAE Training: Train variational autoencoders
Reconstruction Validation: Assess model quality
Trajectory Generation: Create synthetic progression paths
Classification: Stage prediction and biomarker discovery
Enrichment Analysis: Pathway analysis with GSEA

🎨 Visualization¶

Visualization Guide: Create publication-quality figures

For Reproducing Published Results¶

If you want to reproduce the paper:

Option 1: Using Pretrained Models (Recommended)

Follow Using Pretrained Models tutorial
This is the fastest and most accurate way to reproduce results
Uses the exact models from the paper

Option 2: Training from Scratch

Tutorial Conventions¶

Code Blocks¶

Python code to execute:

from renalprog import dataset
data = dataset.load_data('path/to/data.csv')

Shell commands:

python scripts/pipeline_steps/1_data_processing.py

Callouts¶

Note

Informational notes provide additional context.

Tip

Tips offer helpful suggestions and best practices.

Warning

Warnings highlight potential issues or common pitfalls.

Danger

Critical warnings about data loss or major errors.

Example

Example outputs or usage patterns.

File Paths¶

All file paths are relative to the repository root unless otherwise specified:

renalprog/
├── data/
│   ├── raw/           # Downloaded TCGA data
│   ├── interim/       # Intermediate processing outputs
│   └── processed/     # Final processed data
├── models/            # Trained models
├── reports/           # Analysis results
└── scripts/           # Pipeline scripts

Prerequisites¶

Before starting these tutorials, ensure you have:

Installed renalprog (Installation Guide)
Python 3.9+ and R 4.0+ available
(Optional) CUDA-capable GPU for faster training

Getting Help¶

If you encounter issues while following these tutorials:

Review the API Reference for function details
Search GitHub Issues
Ask in GitHub Discussions

Next Steps¶

Ready to begin? Start with the Quick Start Tutorial!