Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling.

ABSTRACT: Building a comprehensive topic model has become an important research tool in single-cell genomics. With a topic model, we can decompose and ascertain distinctive cell topics shared across multiple cells, and the gene programs implicated by each topic can later serve as a predictive model in translational studies. Here, we present a Bayesian topic model that can uncover short-term RNA velocity patterns from a plethora of spliced and unspliced single-cell RNA-sequencing (RNA-seq) counts. We showed that modeling both types of RNA counts can improve robustness in statistical estimation and can reveal new aspects of dynamic changes that can be missed in static analysis. We showcase that our modeling framework can be used to identify statistically significant dynamic gene programs in pancreatic

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10504675 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature