방법 비교

	시계열 대사체학 분석	경로 농축 분석
분야	생물정보학	생물정보학
계열	Process / pipeline	Process / pipeline
기원 연도	2000s–2010s	2003–2005
창시자	Developed from general metabolomics workflows; longitudinal extensions pioneered by A. K. Smilde, R. Bino, and colleagues	Mootha et al. (2003); systematised by Subramanian et al. (2005)
유형	Quantitative longitudinal omics pipeline	Statistical functional annotation method
원전	Smilde, A. K., van der Werf, M. J., Bijlsma, S., van der Werff-van der Vat, B. J. C., & Jellema, R. H. (2005). Fusion of mass spectrometry-based metabolomics data. Analytical Chemistry, 77(20), 6729–6736.	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.
별칭	longitudinal metabolomics, dynamic metabolomics, temporal metabolome profiling, kinetic metabolomics	PEA, overrepresentation analysis, ORA, functional enrichment analysis
관련	6	6
요약	Time-series metabolomics analysis profiles small-molecule metabolites from biological samples collected at multiple, ordered time points, enabling researchers to capture the dynamic flux of metabolic pathways in response to stimuli, disease progression, drug treatment, or developmental change. By integrating longitudinal statistical models with standard metabolomics preprocessing, the approach goes beyond a static metabolic snapshot to reveal how, when, and in what sequence metabolic responses unfold.	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.