scTopoGAN: unsupervised manifold alignment of single-cell data Article Swipe
Akash Singh
,
Marcel Reinders
,
Ahmed Mahfouz
,
Tamim Abdelaal
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.04.27.489829
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1101/2022.04.27.489829
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 [email protected]
Related Topics
Concepts
Autoencoder
Computer science
Modality (human–computer interaction)
Modalities
Manifold (fluid mechanics)
Projection (relational algebra)
Manifold alignment
Topology (electrical circuits)
Artificial intelligence
Nonlinear dimensionality reduction
Pattern recognition (psychology)
Space (punctuation)
Theoretical computer science
Machine learning
Data mining
Deep learning
Dimensionality reduction
Algorithm
Mathematics
Sociology
Social science
Mechanical engineering
Engineering
Combinatorics
Operating system
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2022.04.27.489829
- https://www.biorxiv.org/content/biorxiv/early/2022/04/29/2022.04.27.489829.full.pdf
- OA Status
- green
- Cited By
- 1
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4225282314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4225282314Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1101/2022.04.27.489829Digital Object Identifier
- Title
-
scTopoGAN: unsupervised manifold alignment of single-cell dataWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-04-29Full publication date if available
- Authors
-
Akash Singh, Marcel Reinders, Ahmed Mahfouz, Tamim AbdelaalList of authors in order
- Landing page
-
https://doi.org/10.1101/2022.04.27.489829Publisher landing page
- PDF URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/04/29/2022.04.27.489829.full.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.biorxiv.org/content/biorxiv/early/2022/04/29/2022.04.27.489829.full.pdfDirect OA link when available
- Concepts
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Autoencoder, Computer science, Modality (human–computer interaction), Modalities, Manifold (fluid mechanics), Projection (relational algebra), Manifold alignment, Topology (electrical circuits), Artificial intelligence, Nonlinear dimensionality reduction, Pattern recognition (psychology), Space (punctuation), Theoretical computer science, Machine learning, Data mining, Deep learning, Dimensionality reduction, Algorithm, Mathematics, Sociology, Social science, Mechanical engineering, Engineering, Combinatorics, Operating systemTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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23Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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