---
title: Classical Methods (Transcriptome-centric, 8D)
emoji: 📊
colorFrom: purple
colorTo: blue
sdk: python
tags:
- transcriptomics
- dimensionality-reduction
- pca

license: mit
---

# Classical Dimensionality Reduction (Transcriptome-centric, 8D)

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**: 8
- **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