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Integrating temporal single-cell gene expression modalities for trajectory inference and disease prediction
BACKGROUND: Current methods for analyzing single-cell datasets have relied primarily on static gene expression measurements to characterize the molecular state of individual cells. However, capturing temporal changes in cell state is crucial for the interpretation of dynamic phenotypes such as the c...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2022
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9442962/ https://www.ncbi.nlm.nih.gov/pubmed/36064614 http://dx.doi.org/10.1186/s13059-022-02749-0 |