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README.md

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+---
+title: Classical Methods (Transcriptome-centric, 64D)
+emoji: 📊
+colorFrom: purple
+colorTo: blue
+sdk: python
+tags:
+- transcriptomics
+- dimensionality-reduction
+- pca
+
+license: mit
+---
+
+# Classical Dimensionality Reduction (Transcriptome-centric, 64D)
+
+Pre-trained PCA models for transcriptomics data compression, part of the TRACERx Datathon 2025 project.
+
+## Model Details
+
+- **Methods**: PCA
+- **Compression Mode**: Transcriptome-centric
+- **Output Dimensions**: 64
+- **Training Data**: TRACERx open dataset (VST-normalized counts)
+
+## Contents
+
+The model file contains:
+- **PCA**: Principal Component Analysis model
+- **UMAP**: Uniform Manifold Approximation and Projection model (2-4D only)
+- **Scaler**: StandardScaler fitted on TRACERx data
+- **Feature Order**: Gene/sample order for alignment
+
+## Usage
+
+These models are designed to be used with the TRACERx Datathon 2025 analysis pipeline.
+They will be automatically downloaded and cached when needed.
+
+```python
+import joblib
+
+# Load the model bundle
+model_data = joblib.load("model.joblib")
+
+# Access components
+pca = model_data['pca']
+scaler = model_data['scaler']
+gene_order = model_data.get('gene_order')  # For sample-centric
+
+# Transform new data
+scaled_data = scaler.transform(aligned_data)
+embeddings = pca.transform(scaled_data)
+```
+
+## Training Details
+
+- **Input Features**: 1,051 samples
+- **Training Samples**: 20,136 genes
+- **Preprocessing**: StandardScaler normalization
+
+## Files
+
+- `model.joblib`: Model bundle containing PCA, scaler, and feature order