metabolicpathwayheterogeneity

Metabolic pathway heterogeneity is quantified across scRNA-seq cell groups by computing normalized enrichment scores (NES) for each pathway, followed by PCA and FGSEA to identify subpopulation-specific metabolic states.

PCA and GSEA identify subpopulation-specific metabolic states by applying PCA to pathway NES scores, then running FGSEA on the resulting principal components to highlight significant pathway-PC associations across cell groups.

You can use KEGG or Reactome GMT databases for scRNA-seq metabolic heterogeneity analysis by integrating them via MetabolicInput to flexibly compute and evaluate pathway enrichment scores across defined cell groups.

Metabolic pathway changes across timepoints or treatments are analyzed by computing NES scores across Seurat clustering groups, generating plots and rankings to interpret pathway heterogeneity driven by specific experimental conditions.

Configuring pathway heterogeneity analysis with FGSEA and PCA requires parameters such as select_pcs to define principal components and pathway_pval_cutoff to filter significant pathway-PC associations during the analysis.

Treatment-driven metabolic transitions in single-cell data are best revealed by computing NES scores across Seurat clustering groups and applying PCA to identify variable metabolic pathways responding to specific treatments.