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scTopoGAN: unsupervised manifold alignment of single-cell data.

Akash Singh¹, Kirti Biharie^1,2,3, Marcel J T Reinders^1,2,3

¹Delft Bioinformatics Lab, Delft University of Technology, 2628 XE Delft, The Netherlands.

Bioinformatics Advances

|December 11, 2023

Summary

scTopoGAN enables unsupervised alignment of single-cell multi-omic datasets without overlapping cells or features. This method uses topological autoencoders and generative adversarial networks for robust data integration and enhanced multi-omic interpretation.

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Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell technologies offer deep molecular characterization.
Integrating multi-omic data provides a comprehensive cellular view.
Current integration methods require overlapping cells or features, limiting applications.

Purpose of the Study:

To develop a method for unsupervised manifold alignment of single-cell datasets with non-overlapping cells or features.
To enable integration of diverse single-cell molecular layers without shared data points.

Main Methods:

scTopoGAN utilizes topological autoencoders (topoAE) for modality-specific latent representations.
A topology-guided Generative Adversarial Network aligns these latent spaces into a common manifold.
The method operates in a completely unsupervised setting.

Main Results:

scTopoGAN outperforms existing state-of-the-art manifold alignment methods in unsupervised settings.
Individual topoAEs demonstrate superior preservation of data structure in low-dimensional representations compared to other methods.
The alignment successfully integrates single-cell datasets with non-overlapping features.

Conclusions:

Topology preservation is a powerful concept for aligning multiple single-modality single-cell datasets.
scTopoGAN unlocks the potential for deeper multi-omic interpretations of cellular identity.
The developed method expands the applicability of single-cell data integration.