🧬 MRD Detection in Flow Cytometry using VAE and GMM

This repository presents two complementary approaches for detecting Minimal Residual Disease (MRD) in flow cytometry data:

• VAE/: Deep learning-based anomaly detection using Variational Autoencoders
• GMM/: Probabilistic modeling with Gaussian Mixture Models

---

📊 Dataset Overview

We work with flow cytometry data collected from 12 patients:

• Healthy Patients (P1–P6):
  • ~27 million cells
  • Used for model training
• Unhealthy Patients (P7–P12):
  • ~20 million cells
  • Used for evaluation and MRD prediction

Each cell is represented by 14 features. The models are trained to learn the healthy distribution and detect anomalous cells in new patient data, which may indicate MRD.

---

Objective

To accurately identify cells that are anomalous (i.e., likely cancerous) using only unsupervised learning methods trained on healthy patient data. These anomalies collectively form an estimate of MRD (%).

---

Methods Used

1. Variational Autoencoder (VAE)

• Learns latent representations via probabilistic encoding/decoding
• Detects anomalies based on reconstruction error (MSE)
• Explored different latent dimensions (2 and 4) and β values
• Uses Leave-One-Patient-Out (LOPO) validation with progressive fine-tuning
• Produces per-cell MSE scores → used to estimate MRD

📎 Explore VAE Approach
📎 VAE Best Model

---

2. Gaussian Mixture Model (GMM)

• Trains a mixture of Gaussians on healthy cell data
• Evaluates likelihood of each new cell under the model
• Low-likelihood cells are flagged as anomalies
• Tried multiple component counts (4, 6, 16)
• Compared full vs tied covariance structures
• Final threshold: 1.5th percentile of healthy scores

📎 Explore GMM Approach
📎 GMM Best Model

---

Evaluation Metrics

• Mean Squared Error (MSE) between predicted and actual MRD %
• Mean Absolute Error (MAE)
• MRD Estimation for each patient based on anomaly scores

Both models approximate expert-annotated MRD scores with high accuracy.

---

Main libraries:

• torch
• scikit-learn
• numpy, pandas
• matplotlib, seaborn
• joblib

References

• PyTorch VAE Tutorial
• Uncovering Anomalies with Variational Autoencoders – Towards Data Science
• Hands-On Anomaly Detection with Variational Autoencoders – Medium
• scikit-learn GMM: https://scikit-learn.org/stable/modules/mixture.html
• Understanding Gaussian Mixture Models – Number Analytics Blog
• PMC Article on GMM & MRD