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| Analisi di Arricchimento di Insiemi di Geni (GSEA)× | RNA-seq Differential Expression× | |
|---|---|---|
| Campo | Bioinformatica | Bioinformatica |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) | 2008–2010 (RNA-seq DE methodology established) |
| Ideatore≠ | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Tipo≠ | Functional genomics / enrichment analysis | Quantitative genomics pipeline |
| Fonte seminale≠ | 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 ↗ | Love, M. I., Huber, W., & Anders, S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology, 15(12), 550. DOI ↗ |
| Alias | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Correlati≠ | 5 | 6 |
| Sintesi≠ | 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. | RNA-seq differential expression (DE) analysis identifies genes whose transcript abundance differs significantly between two or more biological conditions — for example, treated versus control, or diseased versus healthy tissue. Starting from raw sequencing reads, the pipeline moves through alignment, count-based normalization, statistical modeling of count dispersion, hypothesis testing, and multiple-testing correction to produce a ranked list of differentially expressed genes accompanied by fold-change estimates and adjusted p-values. |
| ScholarGateInsieme di dati ↗ |
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