Methoden vergleichen
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| Netzwerkbasierte Mikrobiom-Diversitätsanalyse× | Gen-Satz-Anreicherungsanalyse (GSEA)× | |
|---|---|---|
| Fachgebiet | Bioinformatik | Bioinformatik |
| Familie | Process / pipeline | Process / pipeline |
| Entstehungsjahr≠ | 2012 | 2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003) |
| Urheber≠ | Faust, Raes, Friedman, Alm and colleagues | Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute) |
| Typ≠ | Integrative bioinformatics pipeline | Functional genomics / enrichment analysis |
| Wegweisende Quelle≠ | Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687. 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 ↗ |
| Aliasnamen | microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA | GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Network-based microbiome diversity analysis integrates graph-theoretic co-occurrence network inference with classical alpha- and beta-diversity metrics to characterize the structural organization of microbial communities. Rather than treating taxa as independent entities, the method models pairwise microbial associations as edges in a network, enabling identification of keystone taxa, community modules, and ecological interaction patterns that simple diversity indices cannot detect. | 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. |
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