ArticleGenome research2024
A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data.
Article in Genome research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Single-cell analysis of adaptive NK and CMV-specific T-cell dynamics after allogeneic HSCT.Blood advances · 2026Article
- Cell-specific gene expression plasticity in response to hypoxia promotes high-altitude adaptive evolution.Science China. Life sciences · 2026Article
- Single-cell and spatial profiling in cancer biology and clinical oncology.Nature cancer · 2026Review
- Optimal transport fate mapping resolves T cell differentiation dynamics across tissues.bioRxiv : the preprint server for biology · 2026Article
- Learning collective multicellular dynamics with an interacting mean field neural SDE model.PLoS computational biology · 2026Article
- spVelo: RNA velocity inference for multi-batch spatial transcriptomics data.Genome biology · 2025Article
- New Insights and Implications of Cell-Cell Interactions in Developmental Biology.International journal of molecular sciences · 2025Review
- Coordinated immune networks in leukemia bone marrow microenvironments distinguish response to cellular therapy.Science immunology · 2025Article
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11 authors.
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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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