Salīdzināt metodes
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| Diferenciālā ceļu bagātināšanas analīze× | Daudzomu šķērsviršķu bagātības analīze× | |
|---|---|---|
| Nozare | Bioinformātika | Bioinformātika |
| Saime | Process / pipeline | Process / pipeline |
| Izcelsmes gads≠ | 2004–2012 | 2014–2016 (multi-omics extension of enrichment methods established ~2005) |
| Autors≠ | Extended from Over-Representation Analysis (Draghici et al. 2003) and competitive gene-set testing (Smyth lab, ~2004–2012) | Building on Subramanian et al. (2005); multi-omics integration formalised by Meng et al. and others (~2014–2016) |
| Tips≠ | Comparative enrichment analysis | Integrative pathway analysis pipeline |
| Pirmavots≠ | Wu, D., & Smyth, G. K. (2012). Camera: a competitive gene set test accounting for inter-gene correlation. Nucleic Acids Research, 40(17), e133. DOI ↗ | Meng, C., Kuster, B., Culhane, A. C., & Gholami, A. M. (2014). A multivariate approach to the integration of multi-omics datasets. BMC Bioinformatics, 15, 162. link ↗ |
| Citi nosaukumi | differential enrichment analysis, comparative pathway enrichment, DPEA, cross-condition pathway analysis | multi-omics pathway analysis, integrated pathway enrichment, multi-layer pathway enrichment, MOPEA |
| Saistītās≠ | 5 | 1 |
| Kopsavilkums≠ | 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. | Multi-omics pathway enrichment analysis is a bioinformatics pipeline that integrates molecular data from two or more omics layers — such as transcriptomics, proteomics, metabolomics, and epigenomics — and tests whether the combined signal from those layers converges on specific biological pathways more than expected by chance. By considering multiple molecular levels simultaneously, it identifies pathway-level dysregulation that single-omics analyses would miss. |
| ScholarGateDatu kopa ↗ |
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