Vertaile menetelmiä

	Bayesiläinen metabolomiikka-analyysi	Polkurikastusanalyysi
Tieteenala	Bioinformatiikka	Bioinformatiikka
Menetelmäperhe	Process / pipeline	Process / pipeline
Syntyvuosi	2005–2010	2003–2005
Kehittäjä	Simon Rogers, Mark Girolami and colleagues (Bayesian NMR metabolomics framework, ~2009); broader Bayesian metabolomics developed through 2000s–2010s	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tyyppi	Probabilistic statistical pipeline	Statistical functional annotation method
Alkuperäislähde	Rogers, S., Scheltema, R. A., & Girolami, M. A. (2009). Bayesian analysis of metabolomic NMR data. Bioinformatics, 25(14), 1809-1815.	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.
Rinnakkaisnimet	Bayesian metabolomics, probabilistic metabolomics, Bayesian metabolite profiling, Bayesian metabolic flux analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Liittyvät	6	6
Tiivistelmä	Bayesian metabolomics analysis applies probabilistic inference to metabolite abundance data — typically from mass spectrometry or NMR spectroscopy — to identify differentially abundant metabolites, annotate spectral features, and integrate pathway knowledge. By encoding prior biological knowledge into prior distributions and propagating uncertainty throughout the analysis, it yields more calibrated probability statements about metabolic differences than classical frequentist testing alone.	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.