Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Análise Diferencial de Enriquecimento de Vias× | Análise de Enriquecimento de Conjunto de Genes (GSEA)× | |
|---|---|---|
| Área | Bioinformática | Bioinformática |
| Família | Process / pipeline | Process / pipeline |
| Ano de origem≠ | 2004–2012 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Autor original≠ | Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012) | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Tipo≠ | Comparative enrichment analysis | Functional genomics / enrichment analysis |
| Fonte seminal≠ | Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133. DOI ↗ | 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. DOI ↗ |
| Outros nomes | differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Relacionados | 5 | 5 |
| Resumo≠ | Differential pathway enrichment analysis identifies biological pathways whose enrichment signals differ significantly between two or more experimental conditions — for example, between two diseases, two treatments, or two cell types. Rather than asking which pathways are enriched in one condition, it asks which pathways show a statistically meaningful change in enrichment level across conditions, revealing condition-specific or context-dependent biology. | 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. |
| ScholarGateConjunto de dados ↗ |
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