Enrichment analysis and annotation using PAGER
Making Sense of Gene Lists: PAGER and the Interactive Web App
High-throughput experiments, such as RNA-seq and microarray studies, often produce extensive lists of genes. While identifying these genes is an essential first step, the real challenge lies in interpreting their collective biological significance. Which pathways are they associated with? What biological processes or cell types do they represent? Manually searching through databases for this information can be time-consuming and inefficient.
To streamline this important interpretation process, our research group developed PAGER (Pathways, Annotated lists, and Gene-sets Electronic Repository). PAGER serves as a knowledge base containing thousands of “PAGs”—curated sets of genes that represent pathways, annotated lists from publications, protein complexes, cancer signatures, and more. It allows researchers to conduct enrichment analysis by asking: “Are any specific PAGs overrepresented in my gene list?” This analysis helps uncover the biological themes hidden within the data. PAGER has proven to be valuable for tasks such as pathway analysis and annotating cell types in single-cell datasets.
Improving Accessibility: The PAGER Web App
While the PAGER database and analysis methods provide the core foundation, we wanted to make performing these analyses even more straightforward and interactive. That led to the development of the PAGER Web App, an R Shiny-based application. This tool provides a user-friendly graphical interface to:
Upload user-defined gene lists.
Perform PAGER enrichment analysis using various settings.
Explore results through interactive tables and visualizations.
Work with pre-loaded case studies for learning or comparison.
Why Use PAGER (via the Web App)?
PAGER, especially through the interactive web app, helps researchers quickly move from a raw gene list to meaningful biological insights. It simplifies the process of identifying relevant pathways, gene signatures, or potential cell type markers, making functional interpretation of high-throughput data more efficient.
Explore PAGER
Access the main PAGER database & tool: http://discovery.informatics.uab.edu/PAGER/
Find the PAGER Web App code on GitHub: https://github.com/aimed-uab/PAGER-Web-APP
Read our publication on the Web App: Yue, Slominski, Bharti, Chen (2022). Frontiers in Genetics. https://doi.org/10.3389/fgene.2022.820361
We hope these tools help researchers effectively interpret their functional genomics data!
Our Team’s Work & My Contributions
Developing and enhancing the PAGER ecosystem has been a collaborative effort within our lab. My specific contributions have focused on improving the user experience and data visualization:
On the main PAGER platform: I worked on adding interactive summary visualizations for the analysis results, implementing bar plots and scatter plots using the Plotly library, and incorporating network visualizations using D3.js to help users explore the relationships within larger gene sets.
For the PAGER Web App: I was involved in developing the R Shiny application itself, building the interface that allows users to run PAGER analyses more coherently and interactively without needing extensive programming knowledge.
This post was drafted with assistance from various AI models to help share my project work more effectively. Please feel free to reach out if you spot any typos or have corrections!