Comparar métodos

	Análisis de Proteómica Diferencial	Análisis de Enriquecimiento de Vías
Campo	Bioinformática	Bioinformática
Familia	Process / pipeline	Process / pipeline
Año de origen	Late 1990s–2000s (mass spectrometry-based approaches matured ~1999–2004)	2003–2005
Autor original	Pioneered broadly by Matthias Mann and colleagues; SILAC introduced by Ong et al. (2002)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Tipo	Quantitative omics pipeline	Statistical functional annotation method
Fuente seminal	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.
Alias	comparative proteomics, quantitative differential proteomics, differential protein expression analysis, DPA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relacionados	1	6
Resumen	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.