ArchetypeShift: An R Package Integrating KEGG-Informed Pathway Analysis and IPA-Derived Functional Predictions for Validation of Single-Cell Archetypes.

Abstract

Functional archetype analysis of single-cell RNA-sequencing (scRNA-seq) data is important because clustering of cell types is often nuanced and inexact. Intermediate phenotypes exist that are difficult to account for in these analyses, particularly in the setting of infant development. Further, different cell types work together to achieve biological functions, thereby broadly supporting tissue function at homeostasis and through environmental challenges via phenotypic plasticity. Currently, the process of assigning archetypes to cell clusters is labor-intensive because annotation requires manual upload of expression data to multiple programs. ArchetypeShift is an R-based pipeline that integrates existing archetypal analysis methods with annotation by Ingenuity Pathway Analysis (IPA) and Kyoto Encyclopedia of Genes and Genomes (KEGG), graphics visualization, and trajectory analysis for scRNA-seq data. Generating IPA- and/or KEGG-informed dot plots, UMAPs (uniform manifold approximation and projections) of archetype weights, archetype maps, heatmaps defining the top genes for each archetype program, and trajectory analysis graphics, ArchetypeShift streamlines the biological interpretation of archetype programs within a unified and efficient analytical framework. The source code for ArchetypeShift is available on GitHub (https://github.com/Neo-NEC-Lab/ArchetypeShift).