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Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders

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CostaLab/PILOT-GM-VAE

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PILOT-GM-VAE

Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders. We introduce here PatIent-Level Analysis with Optimal Transport based on Gausian Mixture Variational AutoEncoders. PILOT-GM-VAE explores the power of GM-VAE to estimate models describing complex single cell distributions with efficient optimal transport algorithms for estimating the distance between GMs.

plot

git clone https://github.com/CostaLab/PILOT-GM-VAE.git

cd PILOT-GM-VAE

conda create --name PILOT-GM-VAE python=3.11.5 r-base

conda activate PILOT-GM-VAE

pip install .

Navigate to Tutorial.

Then please use the provided Tutorial.

Data sets

You can access the used data sets by PILOT-GM-VAE in Part 1 DOI, Part 2 DOI and Part 3 DOI

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Patient-Level Analysis of Single Cell Disease Atlas with Optimal Transport of Gaussian Mixtures Variational Autoencoders

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