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Codes for paper: CVQVAE: A REPRESENTATION LEARNING METHOD FOR MULTI-OMICS SINGLE CELL DATA INTEGRATION

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CVQVAE

Codes for paper: CVQVAE: A REPRESENTATION LEARNING METHOD FOR MULTI-OMICS SINGLE CELL DATA INTEGRATION. Accepted by MLCB 2022 (Spotlight presentation).

Installation

To install our model, please use:

git clone https://github.com/HelloWorldLTY/CVQVAE.git

Citation

We refer the codes of Vector Quantized Varitional Auto-encoder (VQVAE) model from VQVAE, the codes of Crossing model from CAE, and the codes for benchmark analysis from Benchmark.

Tutorial

Please check our main.py file as a simple tutorial.

Data availability

The download link for benchmark datasets is here.

Reference

If you intend to cite our work, please use this link:

Liu, Tianyu, Grant Greenberg, and Ilan Shomorony. "CVQVAE: A representation learning based method for multi-omics single cell data integration." Machine Learning in Computational Biology. PMLR, 2022.

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Codes for paper: CVQVAE: A REPRESENTATION LEARNING METHOD FOR MULTI-OMICS SINGLE CELL DATA INTEGRATION

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