Enrichment analysis and annotation using PAGER
A database of annotations to enrich gene lists or identify cell types.
High-throughput experiments, such as RNA-seq and microarray studies, often produce extensive lists of genes. Identifying the genes is the easy part; the real challenge is interpreting what they mean together. Which pathways are they associated with? What biological processes or cell types do they represent? Manually searching databases for this is slow.
To make that interpretation faster, 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 surfaces the biological themes hidden in the data. PAGER is useful for pathway analysis and for annotating cell types in single-cell datasets.
Improving Accessibility: The PAGER Web App#
The PAGER database and analysis methods are the core; we wanted running the analyses to be simpler and more interactive. That led to the PAGER Web App, an R Shiny application with a graphical interface to:
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Upload user-defined gene lists.
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Perform PAGER enrichment analysis using various settings.
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Explore results through interactive tables and visualizations.
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Work with pre-loaded case studies for learning or comparison.
Why Use PAGER (via the Web App)?#
PAGER, especially through the web app, gets you from a raw gene list to interpretable biology quickly: relevant pathways, gene signatures, or potential cell type markers, without the manual database crawl.
Explore PAGER#
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Access the main PAGER database & tool: http://discovery.informatics.uab.edu/PAGER/
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Find the PAGER Web App code on GitHub: https://github.com/aimed-uab/PAGER-Web-APP
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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 interpret their functional genomics data.
Our Team’s Work & My Contributions#
The PAGER ecosystem is a lab-wide effort. My contributions focused on 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.