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

ABSTRACT: 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 knowledg

SUBMITTER: Park C 

PROVIDER: S-EPMC10680748 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature