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jruffle/
ae_transcriptome_32d

transcriptomics

dimensionality-reduction

mit

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Autoencoder (Transcriptome-centric, 32D)

Pre-trained Autoencoder model for transcriptomics data compression, part of the TRACERx Datathon 2025 project.

Model Details

Method: Autoencoder
Compression Mode: Transcriptome-centric
Output Dimensions: 32
Training Data: TRACERx open dataset (VST-normalized counts)

Usage

This model is designed to be used with the TRACERx Datathon 2025 analysis pipeline. It will be automatically downloaded and cached when needed.

python

import joblib

# Load the model bundle
model_data = joblib.load("model.joblib")

# Access components based on model type
# See documentation for specific usage

Files

model.joblib: Model bundle containing fitted model and preprocessing parameters

jruffle/ae_transcriptome_32d

Autoencoder (Transcriptome-centric, 32D)

Model Details

Usage

Files

jruffle/
ae_transcriptome_32d