Módszerek összehasonlítása

	Idősoros útvonal-gazdagítási analízis	Génkészlet-gazdagítási elemzés (GSEA)
Tudományterület	Bioinformatika	Bioinformatika
Módszercsalád	Process / pipeline	Process / pipeline
Keletkezés éve	2005–2014	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
Megalkotó	Bar-Joseph and colleagues (temporal gene expression); extended by Cheng, Bhatt et al. for pathway-level time-series inference	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
Típus	Functional enrichment analysis with temporal modeling	Functional genomics / enrichment analysis
Alapmű	Ernst, J., Nau, G. J., & Bar-Joseph, Z. (2005). Clustering short time series gene expression data. Bioinformatics, 21(Suppl 1), i159–i168.	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.
Alternatív nevek	temporal pathway analysis, longitudinal pathway enrichment, dynamic pathway analysis, TPEA	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
Kapcsolódó	5	5
Összefoglaló	Time-series pathway enrichment analysis identifies biological pathways whose coordinated gene activity changes significantly across ordered time points. Rather than treating each time point independently, the method models the temporal trajectory of gene expression within each pathway and tests whether entire biological programs — not just individual genes — are activated or suppressed in a time-dependent manner. It is widely used in developmental biology, drug response studies, and infection time courses.	Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether a predefined set of genes — representing a biological pathway, process, or function — shows statistically significant, coordinated differences between two biological conditions. Unlike simple fold-change filtering, GSEA operates on all measured genes ranked by a correlation metric, detecting subtle but consistent shifts across an entire pathway even when no single gene passes a significance threshold.