metadata
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.
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