To join this seminar virtually: please request Zoom connection details from ea [at] stat.ubc.ca
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. In this talk, I will present a few topic modeling tools for single-cell RNA-sequencing (RNA-seq) data analysis. The first topic model builds on the Embedded Topic Model (ETM) and incorporates sparse-inducing priors to make the model more interpretable. I will showcase that it can be used to unravel cell types or cell states. The second topic model uncovers short-term RNA velocity patterns from a plethora of spliced and unspliced single-cell RNA-sequencing (RNA-seq) counts. I will show 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. I will showcase that our modeling 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.