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ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for
Adam P Wilson1, Hala Chaaban1, Kathryn Y Burge1
1Division of Neonatal-Perinatal Medicine, Department of Pediatrics, University of Oklahoma Health College of Medicine, Oklahoma City, OK, USA.
Bioinformatics and Biology Insights
|August 13, 2026
Summary
ArchetypeShift is a new R-based pipeline that simplifies functional archetype analysis for single-cell RNA sequencing (scRNA-seq) data. It integrates pathway analysis and visualization tools, making cell type annotation more efficient.
Area of Science:
- Computational Biology
- Genomics
- Bioinformatics
Background:
- Functional archetype analysis of single-cell RNA sequencing (scRNA-seq) data is crucial for understanding cell heterogeneity.
- Current methods for cell type annotation are labor-intensive, requiring manual data uploads to multiple programs.
- Intermediate phenotypes and complex cellular interactions in development pose challenges for precise scRNA-seq analysis.
Purpose of the Study:
- To develop an efficient R-based pipeline, ArchetypeShift, for functional archetype analysis of scRNA-seq data.
- To streamline the biological interpretation of cell archetype programs.
- To integrate existing archetypal analysis methods with pathway annotation and visualization tools.
Main Methods:
- ArchetypeShift is an R-based pipeline integrating archetypal analysis with Ingenuity Pathway Analysis (IPA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation.
- The pipeline generates visualizations including IPA/KEGG-informed dot plots, Uniform Manifold Approximation and Projections (UMAPs) of archetype weights, archetype maps, and heatmaps of top genes.
- Trajectory analysis graphics are also produced to aid interpretation.
Main Results:
- ArchetypeShift provides a unified and efficient analytical framework for scRNA-seq data interpretation.
- The pipeline automates and integrates complex analytical steps, reducing manual effort in cell type annotation.
- Generated visualizations facilitate a deeper understanding of archetype programs and their biological significance.
Conclusions:
- ArchetypeShift significantly streamlines the functional archetype analysis of scRNA-seq data.
- The pipeline enhances the biological interpretation of cell clusters and their associated functions.
- ArchetypeShift offers a valuable tool for researchers studying cellular phenotypes and tissue function.
