手法を比較

	ネットワークベースのコピー数変異解析	遺伝子セット濃縮解析 (GSEA)
分野	バイオインフォマティクス	バイオインフォマティクス
系統	Process / pipeline	Process / pipeline
提唱年	2011–2015	2005 (seminal PNAS paper; predecessor concept in Mootha et al. 2003)
提唱者	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, Jill P. Mesirov, Todd R. Golub, Eric S. Lander et al. (Broad Institute)
種類	Computational network analysis pipeline	Functional genomics / enrichment analysis
原典	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.
別名	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	GSEA, gene-set analysis, functional enrichment analysis, pathway-level enrichment
関連	6	5
概要	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.	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.