scTopoGAN: unsupervised manifold alignment of single-cell data

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

Abstract Motivation Single-cell technologies allow deep characterization of different molecular aspects of cells. Integrating these modalities provides a comprehensive view of cellular identity. Current integration methods rely on overlapping features or cells to link datasets measuring different modalities, limiting their application to experiments where different molecular layers are profiled in different subsets of cells. Results We present scTopoGAN, a method for unsupervised manifold alignment of single-cell datasets with non-overlapping cells or features. We use topological autoencoders to obtain latent representations of each modality separately. A topology-guided Generative Adversarial Network then aligns these latent representations into a common space. We show that scTopoGAN outperforms state-of-the-art manifold alignment methods in complete unsupervised settings. Interestingly, the topological autoencoder for individual modalities also showed better performance in preserving the original structure of the data in the low-dimensional representations when compared to other manifold projection methods. Taken together, we show that the concept of topology preservation might be a powerful tool to align multiple single modality datasets, unleashing the potential of multi-omic interpretations of cells. Availability and implementation Implementation available on GitHub ( https://github.com/AkashCiel/scTopoGAN ). All datasets used in this study are publicly available. Contact t.r.m.abdelaal@lumc.nl

Authors 4

1. Akash Kumar Singh

   Delft University of Technology

   Affiliation as printed

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

2. Marcel J. T. Reinders Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

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

   Department of Human Genetics, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, 2ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 333ZC Leiden, The Netherlands

3. Ahmed Essam Mahfouz Aachen Department of Human Genetics Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

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

   Department of Human Genetics, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Department of Human Genetics, Leiden University Medical Center, 2ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 333ZC Leiden, The Netherlands

4. Tamim Abdelaal corresponding Aachen Department of Radiology, Leiden University Medical Center Leiden Computational Biology Center

   Leiden University Medical Center · Delft University of Technology

   Affiliation as printed

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

   LKEB, Department of Radiology, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 2333ZC Leiden, The Netherlands

   Leiden Computational Biology Center, Leiden University Medical Center, 333ZC Leiden, The Netherlands