Σύγκριση μεθόδων

	Single-cell Gene Set Enrichment Analysis	Ανάλυση Εμπλουτισμού Βιολογικών Οδών
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2017-2019	2003–2005
Δημιουργός	Sara Aibar, Stein Aerts (AUCell/SCENIC); David DeTomaso, Nir Yosef (VISION)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Τύπος	Computational enrichment scoring pipeline	Statistical functional annotation method
Θεμελιώδης πηγή	Aibar, S., Gonzalez-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., van den Oord, J., Kalender Atak, Z., Wouters, J., & Aerts, S. (2017). SCENIC: Single-cell regulatory network inference and clustering. Nature Methods, 14(11), 1083-1086.	Subramanian, A., Tamayo, P., Mootha, V. K., Mukherjee, S., Ebert, B. L., Gillette, M. A., Paulovich, A., Pomeroy, S. L., Golub, T. R., Lander, E. S., & Mesirov, J. P. (2005). Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. Proceedings of the National Academy of Sciences, 102(43), 15545–15550.
Εναλλακτικές ονομασίες	scGSEA, single-cell GSEA, cell-level gene set scoring, scRNA-seq pathway scoring	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Συναφείς	5	6
Σύνοψη	Single-cell gene set enrichment analysis (scGSEA) extends classical bulk GSEA to the resolution of individual cells. Rather than testing whether a gene set is enriched in a sample-level comparison, scGSEA assigns an enrichment or activity score to each cell, enabling researchers to map pathway activity across heterogeneous cell populations, cell states, and developmental trajectories captured in single-cell RNA-seq data.	Pathway enrichment analysis (PEA) is a statistical approach that takes a list of genes or proteins of interest — typically derived from a differential expression or proteomics experiment — and identifies which pre-defined biological pathways or functional gene sets are represented more often than expected by chance. By mapping individual molecular changes onto curated pathway knowledge bases such as KEGG, Gene Ontology, or Reactome, PEA translates long gene lists into interpretable biological processes, making it a central tool in the post-analysis of high-throughput omics experiments.