পদ্ধতির তুলনা করুন

	নেটওয়ার্ক-ভিত্তিক কপি নম্বর ভ্যারিয়েশন বিশ্লেষণ	পাথওয়ে এনরিচমেন্ট বিশ্লেষণ
ক্ষেত্র	জৈব তথ্যবিজ্ঞান	জৈব তথ্যবিজ্ঞান
পরিবার	Process / pipeline	Process / pipeline
উদ্ভবের বছর	2011–2015	2003–2005
প্রবর্তক	Fabio Vandin, Benjamin Raphael and colleagues (HotNet framework); Matthew Leiserson et al. (HotNet2)	Mootha et al. (2003); systematised by Subramanian et al. (2005)
ধরন	Computational network analysis pipeline	Statistical functional annotation method
মৌলিক উৎস	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.
অপর নাম	network CNV analysis, CNV network propagation, graph-based CNV analysis, network-integrated copy number analysis	PEA, overrepresentation analysis, ORA, functional enrichment analysis
সম্পর্কিত	6	6
সারসংক্ষেপ	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.