RT Journal
A1 Park, Cameron Y
A1 Mani, Shouvik
A1 Beltran-Velez, Nicolas
A1 Maurer, Katie
A1 Huang, Teddy
A1 Li, Shuqiang
A1 Gohil, Satyen
A1 Livak, Kenneth J
A1 Knowles, David A
A1 Wu, Catherine J
A1 Azizi, Elham
T1 A Bayesian framework for inferring dynamic intercellular interactions from time-series single-cell data
JF Genome Research 
JO Genome Research 
YR 2024 
FD September 05 
DO 10.1101/gr.279126.124 
SP gr.279126.124 
UL http://genome.cshlp.org/content/early/2024/09/04/gr.279126.124.abstract 
AB 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 existing 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 co-cultured with lymphoma cells, demonstrating its potential to uncover dynamic cell-cell crosstalk.