Comparer des méthodes

	Analyse de la diversité du microbiome basée sur les réseaux	Analyse d'enrichissement de voies
Domaine	Bio-informatique	Bio-informatique
Famille	Process / pipeline	Process / pipeline
Année d'origine	2012	2003–2005
Auteur d'origine	Faust, Raes, Friedman, Alm and colleagues	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Integrative bioinformatics pipeline	Statistical functional annotation method
Source fondatrice	Friedman, J., & Alm, E. J. (2012). Inferring correlation networks from genomic survey data. PLoS Computational Biology, 8(9), e1002687.	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.
Alias	microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Apparentées	5	6
Résumé	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.	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.