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| Pathway Enrichment Analysis× | RNA-seq Differential Expression× | |
|---|---|---|
| Fagområde | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Oprindelsesår≠ | 2003–2005 | 2008–2010 (RNA-seq DE methodology established) |
| Ophavsperson≠ | Mootha et al. (2003); systematised by Subramanian et al. (2005) | Multiple groups; foundational methods from Anders & Huber (DESeq, 2010), Robinson, McCarthy & Smyth (edgeR, 2010) |
| Type≠ | Statistical functional annotation method | Quantitative genomics pipeline |
| Oprindelig kilde≠ | 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 ↗ |
| Aliasser | PEA, overrepresentation analysis, ORA, functional enrichment analysis | RNA-seq DE analysis, transcriptomic differential expression, bulk RNA-seq DE, DEA |
| Relaterede | 6 | 6 |
| Resumé≠ | 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. | 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. |
| ScholarGateDatasæt ↗ |
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