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DIISCO: A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data

GSE255888 Homo sapiens Expression profiling by high throughput sequencing 4 samples Submitted 2024/09/02 Platform GPL24676
Summary
Characterizing cell-cell communication and tracking its variability over time is essential for understanding the coordination of biological processes mediating normal development, progression of disease, or responses to perturbations such as therapies. Existing tools lack the ability to capture time-dependent intercellular interactions, such as those influenced by therapy, and primarily rely on existing databases compiled from limited contexts. We present DIISCO, a Bayesian framework for characterizing the temporal dynamics of cellular interactions using single-cell RNA-sequencing data from multiple time points. Our method uses structured Gaussian process regression to unveil time-resolved interactions among diverse cell types according to their co-evolution and incorporates prior knowledge of receptor-ligand complexes. We show the interpretability of DIISCO in simulated data and new data collected from CAR-T cells co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.
Published in
A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
Park C, Mani S, Beltran-Velez N et al. · Genome research 2024 · PMID 39237300 · doi:10.1101/gr.279126.124
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Also filed as BioProject PRJNA1076917 and SRA study SRP489921. Searching any of these in the dataset finder brings you back here.

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