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bioRxiv · 10.1101/2022.02.14.480323

uniPort: a unified computational framework for single-cell data integration with optimal transport

Cao, K.·Gong, Q.·Hong, Y.·Wan, L.
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Abstract

Single-cell data integration can provide a comprehensive molecular view of cells. Here we introduce uniPort, a unified single-cell data integration framework which combines a coupled Variational Autoencoder (coupled-VAE) and Minibatch Unbalanced Optimal Transport (Minibatch-UOT). It leverages both highly variable common and dataset-specific genes for integration and is scalable to large-scale and partially overlapping datasets. uniPort jointly embeds heterogeneous single-cell multi-omics datasets into a shared latent space. It can further construct a reference atlas for online prediction across datasets. Meanwhile, uniPort provides a flexible label transfer framework to deconvolute spatial heterogeneous data using optimal transport space, instead of embedding latent space. We demonstrate the capability of uniPort by integrating a variety of datasets, including single-cell transcriptomics, chromatin accessibility and spatially resolved transcriptome data. uniPort software is available at https://github.com/caokai1073/uniPort.

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Cao, K., Gong, Q., Hong, Y., Wan, L.. 2022-02-15. uniPort: a unified computational framework for single-cell data integration with optimal transport. https://doi.org/10.1101/2022.02.14.480323

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