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A unified computational framework for single-cell data integration with optimal transport.

Kai Cao^1,2, Qiyu Gong³, Yiguang Hong⁴

¹LSC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.

Nature Communications

|December 1, 2022

Summary

This summary is machine-generated.

uniPort integrates diverse single-cell multi-omics and spatial transcriptomic data using a coupled variational autoencoder and optimal transport. This framework handles data heterogeneity and scales to large datasets, enabling gene imputation and spatial deconvolution.

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Area of Science:

Computational Biology and Bioinformatics
Genomics and Transcriptomics
Single-Cell Analysis

Background:

Integrating heterogeneous single-cell multi-omics and spatially resolved transcriptomic data is crucial for a comprehensive cellular molecular view.
Existing methods face challenges in handling the heterogeneity and scale of diverse single-cell datasets.

Purpose of the Study:

To introduce uniPort, a unified framework for integrating diverse single-cell data types.
To address the challenge of heterogeneity in single-cell multi-omics and spatial transcriptomics integration.
To enable scalable integration of large-scale single-cell datasets.

Main Methods:

Developed uniPort, a framework combining a coupled variational autoencoder (coupled-VAE) and minibatch unbalanced optimal transport (Minibatch-UOT).
Leveraged highly variable common and dataset-specific genes for robust data integration.
Employed optimal transport for flexible label transfer in spatial transcriptomic data deconvolution.

Main Results:

uniPort successfully integrates heterogeneous single-cell multi-omics datasets into a shared latent space.
The framework enables gene imputation across datasets by constructing a reference atlas.
uniPort effectively deconvolutes spatial transcriptomic data using an optimal transport plan.

Conclusions:

uniPort provides a scalable and unified solution for integrating diverse single-cell data, including multi-omics and spatial transcriptomics.
The framework's ability to handle heterogeneity and perform gene imputation and spatial deconvolution advances single-cell data analysis.
Demonstrated uniPort's versatility across single-cell transcriptomics, chromatin accessibility, and spatial transcriptomic data integration.