Σύγκριση μεθόδων

	Ανάλυση Μεταβολομικής Μοναδιαίων Κυττάρων	Ανάλυση Εμπλουτισμού Βιολογικών Οδών
Πεδίο	Βιοπληροφορική	Βιοπληροφορική
Οικογένεια	Process / pipeline	Process / pipeline
Έτος προέλευσης	2013–2021 (emerging field; major methods established ~2019–2021)	2003–2005
Δημιουργός	Multiple groups; key early platforms: Alexandrov lab (SpaceM), Bhatt/Bhattacharya groups	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Τύπος	Analytical pipeline	Statistical functional annotation method
Θεμελιώδης πηγή	Rappez, L., Stadler, M., Triana, S., Gathungu, R. M., Ovchinnikova, K., Phapale, P., Heikenwalder, M., & Alexandrov, T. (2021). SpaceM reveals metabolic states of single cells. Nature Methods, 18(7), 799–805.	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	scMetabolomics, single-cell metabolic profiling, single-cell mass spectrometry metabolomics, SC-MS metabolomics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Συναφείς	4	6
Σύνοψη	Single-cell metabolomics analysis measures the small-molecule metabolite content of individual cells, revealing cell-to-cell metabolic heterogeneity that bulk methods obscure by averaging. Rooted in mass spectrometry and microfluidics advances, it enables researchers to map metabolic states across cell populations, identify rare subpopulations, and link metabolic phenotypes to cellular function — providing a functional complement to transcriptomics and proteomics at single-cell resolution.	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.