Unraveling dynamically-encoded latent transcriptomic patterns in pancreatic cancer cells by topic modelling

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-seq counts. We showed that modelling both types of RNA counts can improve robustness in statistical estimation and reveal new aspects of dynamic changes that can be missed in static analysis. We showcase that our modelling framework can be used to identify statistically-significant dynamic gene programs in pancreatic cancer data. Our results discovered that seven dynamic gene programs (topics) are highly correlated with cancer prognosis and generally enrich immune cell types and pathways.