Matching single cells across modalities with contrastive learning and optimal transport Article Swipe
Federico Gossi
,
Pushpak Pati
,
Panagiotis Chouvardas
,
Adriano Martinelli
,
Marianna Kruithof‐de Julio
,
Maria Anna Rapsomaniki
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad130
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbad130
Understanding the interactions between the biomolecules that govern cellular behaviors remains an emergent question in biology. Recent advances in single-cell technologies have enabled the simultaneous quantification of multiple biomolecules in the same cell, opening new avenues for understanding cellular complexity and heterogeneity. Still, the resulting multimodal single-cell datasets present unique challenges arising from the high dimensionality and multiple sources of acquisition noise. Computational methods able to match cells across different modalities offer an appealing alternative towards this goal. In this work, we propose MatchCLOT, a novel method for modality matching inspired by recent promising developments in contrastive learning and optimal transport. MatchCLOT uses contrastive learning to learn a common representation between two modalities and applies entropic optimal transport as an approximate maximum weight bipartite matching algorithm. Our model obtains state-of-the-art performance on two curated benchmarking datasets and an independent test dataset, improving the top scoring method by 26.1% while preserving the underlying biological structure of the multimodal data. Importantly, MatchCLOT offers high gains in computational time and memory that, in contrast to existing methods, allows it to scale well with the number of cells. As single-cell datasets become increasingly large, MatchCLOT offers an accurate and efficient solution to the problem of modality matching.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbad130
- https://academic.oup.com/bib/article-pdf/24/3/bbad130/50417317/bbad130.pdf
- OA Status
- bronze
- Cited By
- 10
- References
- 54
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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- DOI
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https://doi.org/10.1093/bib/bbad130Digital Object Identifier
- Title
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Matching single cells across modalities with contrastive learning and optimal transportWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-29Full publication date if available
- Authors
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Federico Gossi, Pushpak Pati, Panagiotis Chouvardas, Adriano Martinelli, Marianna Kruithof‐de Julio, Maria Anna RapsomanikiList of authors in order
- Landing page
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https://doi.org/10.1093/bib/bbad130Publisher landing page
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https://academic.oup.com/bib/article-pdf/24/3/bbad130/50417317/bbad130.pdfDirect link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://academic.oup.com/bib/article-pdf/24/3/bbad130/50417317/bbad130.pdfDirect OA link when available
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Modalities, Matching (statistics), Computer science, Artificial intelligence, Natural language processing, Mathematics, Statistics, Sociology, Social scienceTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 4, 2024: 4, 2023: 2Per-year citation counts (last 5 years)
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54Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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