Sammenlign metoder

	Netværksbaseret analyse af kopitalvarians	Pathway Enrichment Analysis
Fagområde	Bioinformatik	Bioinformatik
Familie	Process / pipeline	Process / pipeline
Oprindelsesår	2011–2015	2003–2005
Ophavsperson	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
Type	Computational network analysis pipeline	Statistical functional annotation method
Oprindelig kilde	Vandin, F., Upfal, E., & Raphael, B. J. (2012). De novo discovery of mutated driver pathways in cancer. Genome Research, 22(2), 375–385.	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	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
Relaterede	6	6
Resumé	Network-based copy number variation analysis integrates genome-wide CNV data with biological interaction networks — such as protein-protein interaction (PPI) or pathway networks — to identify functionally coherent regions, driver genes, and altered subnetworks that raw CNV calling alone would miss. By propagating CNV signals through the network graph, the method reveals coordinated genomic dosage imbalances that converge on common biological functions, making it especially powerful in cancer genomics and rare-disease studies.	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.