方法对比

	差异蛋白质组学分析	通路富集分析
领域	生物信息学	生物信息学
方法族	Process / pipeline	Process / pipeline
起源年份	Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)	2003–2005
提出者	Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
类型	Quantitative omics pipeline	Statistical functional annotation method
开创性文献	Ong, S.-E., Blagoev, B., Kratchmarova, I., Kristensen, D. B., Steen, H., Pandey, A., & Mann, M. (2002). Stable isotope labeling by amino acids in cell culture, SILAC, as a simple and accurate approach to expression proteomics. Molecular & Cellular Proteomics, 1(5), 376–386.	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.
别名	comparative proteomics, quantitative differential proteomics, differential protein expression analysis, DPA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
相关	1	6
摘要	Differential proteomics analysis is a quantitative pipeline that identifies proteins whose abundance levels change significantly between two or more biological conditions — such as healthy versus diseased tissue, treated versus untreated cells, or different developmental stages. By combining mass spectrometry-based detection with statistical testing, the method generates ranked lists of differentially expressed proteins that can be linked to biological pathways, disease mechanisms, or drug targets.	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.