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A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.
Park, Cameron; Mani, Shouvik; Beltran-Velez, Nicolas; Maurer, Katie; Huang, Teddy; Li, Shuqiang; Gohil, Satyen; Livak, Kenneth J; Knowles, David A; Wu, Catherine J; Azizi, Elham.
Afiliación
  • Park C; Department of Biomedical Engineering, Columbia University, New York, New York 10027, USA; cyp2111@columbia.edu ea2690@columbia.edu.
  • Mani S; Irving Institute for Cancer Dynamics, Columbia University, New York, New York 10027, USA.
  • Beltran-Velez N; Irving Institute for Cancer Dynamics, Columbia University, New York, New York 10027, USA.
  • Maurer K; Department of Computer Science, Columbia University, New York, New York 10027, USA.
  • Huang T; Department of Computer Science, Columbia University, New York, New York 10027, USA.
  • Li S; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
  • Gohil S; Harvard Medical School, Boston, Massachusetts 02115, USA.
  • Livak KJ; Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA.
  • Knowles DA; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
  • Wu CJ; Translational Immunogenomics Laboratory, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
  • Azizi E; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts 02215, USA.
Genome Res ; 34(9): 1384-1396, 2024 Oct 11.
Article en En | MEDLINE | ID: mdl-39237300
ABSTRACT
Characterizing cell-cell communication and tracking its variability over time are crucial for understanding the coordination of biological processes mediating normal development, disease progression, and responses to perturbations such as therapies. Existing tools fail to capture time-dependent intercellular interactions and primarily rely on databases compiled from limited contexts. We introduce DIISCO, a Bayesian framework designed to characterize the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method utilizes structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their coevolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from T cells cocultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell cross talk.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación Celular / Teorema de Bayes / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Comunicación Celular / Teorema de Bayes / Análisis de la Célula Individual Límite: Humans Idioma: En Revista: Genome Res Asunto de la revista: BIOLOGIA MOLECULAR / GENETICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos