NeuroVelo: interpretable learning of cellular dynamics from single-cell transcriptomic data
Abstract Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity, often in combination with non-linear dimensionality reduction, have been proposed. However, interpreting their results in the light of the underlying biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with a non-linear low-dimensional dynamical system. Using dynamical systems theory, NeuroVelo can then identify genes and biological processes driving temporal cellular dynamics. We benchmark NeuroVelo against several current methods using single-cell multi-omic data, demonstrating that NeuroVelo is superior to competing methods in terms of identifying biological pathways and reconstructing evolutionary dynamics..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
bioRxiv.org - (2024) vom: 15. Feb. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Kouadri Boudjelthia, Idris [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| doi: |
10.1101/2023.11.17.567500 |
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| PPN (Katalog-ID): |
XBI041572955 |
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| 520 | |a Abstract Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity, often in combination with non-linear dimensionality reduction, have been proposed. However, interpreting their results in the light of the underlying biology remains difficult, and their predictive power is limited. Here we propose NeuroVelo, a method that couples learning of an optimal linear projection with a non-linear low-dimensional dynamical system. Using dynamical systems theory, NeuroVelo can then identify genes and biological processes driving temporal cellular dynamics. We benchmark NeuroVelo against several current methods using single-cell multi-omic data, demonstrating that NeuroVelo is superior to competing methods in terms of identifying biological pathways and reconstructing evolutionary dynamics. | ||
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| 700 | 1 | |a Milite, Salvatore |0 (orcid)0000-0002-0156-3636 |4 aut | |
| 700 | 1 | |a El Kazwini, Nour |0 (orcid)0000-0003-2165-8875 |4 aut | |
| 700 | 1 | |a Fernandez-Mateos, Javier |4 aut | |
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| 700 | 1 | |a Huang, Yuanhua |0 (orcid)0000-0003-3124-9186 |4 aut | |
| 700 | 1 | |a Sottoriva, Andrea |0 (orcid)0000-0001-6709-9533 |4 aut | |
| 700 | 1 | |a Sanguinetti, Guido |0 (orcid)0000-0002-6663-8336 |4 aut | |
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