Sammenlign metoder

	Netværksbaseret analyse af mikrobiel diversitet	Pathway Enrichment Analysis
Fagområde	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2012	2003–2005
Ophavsperson	Faust, Raes, Friedman, Alm and colleagues	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Integrative bioinformatics pipeline	Statistical functional annotation method
Oprindelig kilde	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.
Aliasser	microbial co-occurrence network analysis, microbiome network ecology, ecological network-based diversity, NBMDA	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relaterede	5	6
Resumé	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.