ArticleCell genomics2023
Unraveling dynamically encoded latent transcriptomic patterns in pancreatic cancer cells by topic modeling.
Article in Cell genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- scENature communications · 2026Article
- Exploring the latent space of transcriptomic data with topic modeling.NAR genomics and bioinformatics · 2025Article
- A scalable approach to topic modelling in single-cell data by approximate pseudobulk projection.Life science alliance · 2024Article
- Assessing the evolution of research topics in a biological field using plant science as an example.PLoS biology · 2024Article
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Authors and funding
3 authors.
Funding
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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 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.
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Registered trials
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