TY  - JOUR
A1  - Park, Cameron
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
Y1  - 2024/09/01 
JF  - Genome Research 
JO  - Genome Research 
SP  - 1384 
EP  - 1396 
DO  - 10.1101/gr.279126.124 
VL  - 34 
IS  - 9 
UR  - http://genome.cshlp.org/content/34/9/1384.abstract 
N2  - 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. 
ER  -